Abstract— This article presents a comprehensive survey of the
literature on self-interference management schemes required to
achieve a single frequency full duplex communication in wireless
communication networks. A single frequency full duplex system
often referred to as in-band full duplex (FD) system has emerged
as an interesting solution for the next generation mobile networks
where scarcity of available radio spectrum is an important issue.
Although studies on the mitigation of self-interference have been
documented in the literature, this is the first holistic attempt at
presenting not just the various techniques available for handling
self-interference that arises when a full duplex device is enabled,
as a survey, but it also discusses other system impairments that
significantly affect the self-interference management of the system,
and not only in terrestrial systems, but also on satellite
communication systems. The survey provides a taxonomy of self-
interference management schemes and shows by means of
comparisons the strengths and limitations of various self-
interference management schemes. It also quantifies the amount
of self-interference cancellation required for different access
schemes from the 1st generation to the candidate 5th generation of
mobile cellular systems. Importantly, the survey summarises the
lessons learnt, identifies and presents open research questions and
key research areas for the future. This paper is intended to be a
guide and take off point for further work on self-interference
management in order to achieve full duplex transmission in mobile
networks including heterogeneous cellular networks which is
undeniably the network of future wireless systems.
Index Terms—5G, Active Interference Cancellation, Full
Duplex, Passive Interference Mitigation, Remote Radio Heads,
Self-interference Cancellation.
I. INTRODUCTION
HE sustained advancement in the digital world economy
and the evolution of the mobile cellular and wireless
networks has led to increased global mobile traffic [1-3]. With
the proliferation of smart devices (e.g., smart phones, tablet
computers, and Internet of Things (IoT) devices) and the race
for 5G at an advanced stage, capacity issues on wireless
communication systems needs to be addressed [4]. A promising
+C. D. Nwankwo, *L. Zhang, +A. Quddus, *M. A. Imran and +R. Tafazolli +Institute for Communication Systems, 5G Innovation Centre (5GIC), University of Surrey, Guildford, GU2 7XH, United Kingdom. (e-mail:
{c.nwankwo, a.quddus, r.tafazolli} @surrey.ac.uk).
emergent technology in this regard is the in-band Full Duplex
(FD) system which is capable of potentially doubling the
capacity or Spectral Efficiency (SE) of the current wireless
communication systems. FD operation entails enabling wireless
terminals to transmit and receive signals at the same time over
the same frequency band. Enabling FD operation in a wireless
network involves the radios operating in FD mode. In this
mode, Self-Interference (SI) will occur. SI here refers to the
receive antennas capturing the interfering signals from their
own transmit antennas as well as receiving the interference,
noise and useful signals from other radios. Fig. 1 depicts a
single cell FD base station (BS) serving two HD user equipment
(UE), with both operating on same frequency - one on the
downlink (DL) designated as UE1 and the other on the uplink
(UL) designated as UE2. The SI leaking from the transmit path
to the receive path as well as the UL to DL interference
constitute nuisance to the users. The SI can be several millions
stronger than the desired signal due to the short distance
between transmit and receive antennas at the BS [5-7].
UE2: UL BS
UL-DL Intereference
Self interference UE1: DL
Fig. 1. Full duplex enabled BS showing effects of self-interference
Managing interference in current half duplex (HD) wireless
networks is already a significant issue which becomes more
pronounced and challenging heterogeneous cellular
deployment scenarios that involve multi-source and multi-
destination channels. Enabling FD operation in wireless
* School of Engineering, University of Glasgow, G12 8QQ, United Kingdom
(e-mail: {lei.zhang, muhammad.imran}@glasgow.ac.uk).
A Survey of Self-Interference Management
Techniques for Single Frequency Full Duplex
Systems
Chinaemerem David Nwankwo, Student Member, IEEE, Lei Zhang, Atta Quddus, Muhammad Ali
Imran, Senior Member, IEEE, and Rahim Tafazolli, Senior Member, IEEE
T
communication networks will make this problem even more
critically important due to SI.
Communication can either be simplex or duplex. A simplex
communication is simply a one-way communication.
Conversely, a duplex communication involves connected
parties capable of communicating with one another in both
forward and reverse directions [8-9]. Duplex systems are
presented in two forms: HD communication and FD
communication. Depending on capabilities and
configurations, a Mobile Terminal (MT) – used
interchangeably with UE - is capable of either HD or FD
operation in some frequency bands [9]. Conventionally, most
currently deployed communication terminals operate in HD
mode, separating the transmission and reception in either
frequency or time domain [10-11]. HD is a bidirectional
transmission based on two orthogonal channels typically using
time (i.e., Time Division Duplex (TDD)) or frequency (i.e.,
Frequency Division Duplex (FDD)) dimensions, to provide
separation between transmit and receive signals [11-12]. This
therefore means available communication resources are not
efficiently utilized leading to loss and inefficiency in spectrum
usage.
As service demand profiles evolve over the wireless
networks, there is increased need to provide a far more efficient
and reliable mobile communication typified by higher data rates
and SE than is presently obtainable with HD systems. A
possibility for efficient spectrum management is developing a
technique capable of enabling Simultaneous Transmission and
Reception (STR) of radio signals on the same frequency and
time resources [13]. This is called FD communication. In
literature, this concurrent transmission and reception using
same frequency is also referred to as in-band full duplexing [14-
18]. FD is defined in [19] as “the ability of a wireless terminal
to transmit and receive simultaneously over non-orthogonal
channels which could potentially double the available spectrum
and subsequently increase the data rates.”
Beyond potentially doubling the SE of current HD wireless
communication systems; FD offers more flexibility in spectrum
usage. It is capable of improving security of data during
transmission and also able to reduce the air interface latency
and delays. In addition, FD systems are capable of solving end-
to-end delays in wireless networks [11] as well as the hidden
node problems [3], [6]. The implementation of Medium Access
Control (MAC) allows each node to transmit simultaneously
meaning the system is designed not to permit hidden nodes.
With instantaneous retransmission without intermittent
stoppages for either the transmission duration or the reception
duration, latency is improved. FD can improve ad-hoc, mesh
and relay networks and could enable the introduction of novel,
flexible and efficient channel access mechanisms. However,
achieving these potentials of FD is only possible if the threat
posed by the catastrophic effects of SI is adequately managed.
For improved performance over other current HD technologies
which also have potential for huge capacity increases such as
Multiple Input Multiple Output (MIMO) techniques, SI needs
1 It is almost impossible to completely cancel self-interference, but for the
sake of this survey, we shall be using self-interference cancellation and self-
interference mitigation or management interchangeably to mean the same thing.
to be mitigated at least to the noise floor or reasonably close to
it [15], [20].
Several solutions for SI management1 have been proposed
and discussed in literature. Whereas some of these hinged on
the domain the mitigation takes place, others are more about the
mechanism employed. Current SI management techniques
involve passive methods followed by active analogue and
active digital mitigation schemes. The active analogue schemes
mainly eliminate SI by signals inversion using extra hardware
in generating the cancellation signals. The active digital
schemes are promising but linear distortion and noise coupled
with high complexities make the digital cancellation costly. For
this reasons, in addition to comprehensively examining current
SI mitigation schemes, the paper shall identify some open
research questions and future research directions in areas it
believes require further evaluation and research in a bid to
realising a feasible, practical, cost-effective SI management
scheme that could enable FD for the future wireless
communication systems.
A number of FD-related projects have been launched geared
at realising the next generation of wireless communication
systems. These include: Adaptive, Heterogeneous, Incentive-
Compatible, Localized and Secure Networking (AGILENET)
[21] and Full-Duplex Radios for Local Access (DUPLO) [22].
AGILENET was set up mainly to coordinate research in the
field of Cognitive Radio Networking (CRN). It benefited from
some of the standardisation work done by 5th Generation Non-
Orthogonal Waveforms for Asynchronous Signaling
(5GNOW) and Mobile and wireless communications Enablers
for Twenty-twenty (2020) Information Society (METIS) [21].
The programme concerned its work particularly with FD
operation and extended the state-of-the-art in spectrum
utilisation. DUPLO project is involved with research on FD
systems. The project focuses on the new FD radio transmission
paradigm which enables STR on same carrier at the same time
and aims at developing new systems and technologies for future
networks by introducing the FD radio transmission model
mostly focusing on small area radio communication systems
and networks such as femto cells, pico cells, metro cells and
micro cells.
The contributions of this paper includes the following:
1) Comparison of full duplex self-interference management
systems in terms of their potentials and
limitations/constraints.
2) The classifications / taxonomy of the self-interference
mitigation schemes in two different formats – domain-
based and non-domain-based.
3) The state of the art potential of SIC schemes as well as
comparison of different self-interference cancellation
schemes and their capabilities.
4) The paper also presents the required amount of cancellation
needed to enable full duplex in different technologies and
access schemes from 1st to 5th generation of wireless
communication systems.
5) Discussions of self-interference mitigation schemes for
satellite communication systems.
6) Reviewing the plethora of literature available on full
duplex systems and self-interference mitigation schemes,
the authors gleaned the lessons learnt and appropriately
suggested recommendations for researching the subject
further.
The rest of the paper is organised as follows:
In Section II the key points of published survey papers
relevant to FD and SI mitigation are reviewed and presented.
The aim is to highlight what has been studied in literature with
regards to different available techniques for SI management. As
an outcome of this exploration, it is found that key gap in the
available literature is the amount of SI cancellation required
(and the corresponding SI mitigation methods) to enable FD for
different access schemes (for terrestrial as well as satellite
communication). In this Section we also present the trend for SI
management in early FD systems.
Section III reviews and presents the different classification
of SI mitigation schemes and their capabilities, including
mitigation schemes for satellite communication (SatCom). It
also discusses the advantages and disadvantages of the
schemes.
In section IV, we discuss the effects of transceiver
impairments on the SI cancellation abilities of the various SI
mitigation schemes as well as model the analogue circuit
distortions caused by these impairments.
Section V discusses the SI issues with MIMO systems
starting with identifying extra interferences on the systems as a
result of FD operations, then progressing to a simple modelling
perspective of the multi-antenna systems before calculating and
presenting the amount of SI cancellation needed to enable FD
operations for various technologies. The section concludes by
discussing some MIMO-assisted SI mitigation schemes.
In Section VI, the highlights of the possible challenges of SI
mitigation in multi-cell wireless communication systems citing
examples with full- duplex relay networks as well as cellular
Heterogeneous Networks (HetNets) is presented.
Finally in Section VII is the summary of the lessons leant and
subsequently some open research issues and future direction as
well as conclusion of the survey are presented.
Notation: Standard notations are employed in this paper.
Non-bold variables denote scalars, bold lower case variables
represent vectors while bold upper case variables represent
matrices. For any general matrix H, 𝐇† refers to the conjugate
transpose. INR is the 𝐍𝐱𝐑 Identity matrix. Furthermore, we
shall use MT, UE and Users interchangeably to refer to mobile
devices throughout the paper.
TABLE I
LIST OF ACRONYMS AND DEFINITIONS
Acronyms Definition
ADC Analogue-to-Digital Converter AMPS Advanced Mobile Phone Service
AGILENET Adaptive, Heterogeneous, Incentive-Compatible,
Localized and Secure Networking BDMA Beam Division Multiple Access
BS Base Station
CDMA Code Division Multiple Access
CM Common Mode
CRN Cognitive Radio Networking
CSI Channel State Information
CW Continuous Wave DAC Digital-to-Analogue Converter
dB Decibel
dBm Decibel-metre DL Downlink
DM Differential Mode
DSC Digital Self-Interference Canceller DSP Digital Signal Processing
DUPLO Full duplex Radios for Local Access
DVB-S2 Digital Video Broadcasting – Satellite - Second Generation
D2D Device-to-Device
EBD Electrical Balance Duplexer EDGE Enhanced Data Rate for GSM Evolution
EVDO Evolution-Data Optimised
EVM Error Vector Magnitude FBMC Filter Bank Multi-Carrier
FD Full Duplex
FDD Frequency Division Duplexing FDBM Full Duplex Block Markov
FDMA Frequency Division Multiple Access
FDMH Full Duplex Multi-Hop FDR Full Duplex Relay
GEO Geosynchronous Equatorial Orbit
GW Gateway GPRS General Packet Radio Service
GSM Global Systems for Mobile Communications
HD Half Duplex HDR Half Duplex Relay
HetNets Heterogeneous Networks
HPA High Power Amplifier HSDPA High speed Downlink Packet Access
HSUPA High speed Uplink Packet Access
Hz Hertz IBFD In-Band Full Duplex
IEEE Institute of Electrical and Electronic Engineers
IoT Internet of Things I/Q In-phase Quadrature
LEO Low Earth Orbit
LNA Low Noise Amplifier LOS Line of Sight
LTE Long Term Evolution
LTE-A Long Term Evolution –Advanced MAC Medium Access Control
MEO Medium Earth Orbit
METIS Mobile and wireless communications Enablers for
Twenty-twenty (2020) Information Society
MIMO Multiple-Input Multiple-Output MMIC Monolithic Microwave Integrated Circuits
MOP Minimum Output Power
MP Memory Polynomial MT Mobile Terminal
OFDM Orthogonal Frequency Division Multiplexing
PLR Packet Loss Rate PN Phase Noise
RF Radio Frequency
RMS Root Mean Square RRA Radio Resource Allocation
RRM Radio Resource Management
RX Receive SatCom Satellite Communication
SC-FDMA Single Carrier Frequency Division Multiple Access
SDR Software Defined Radio SE Spectral Efficiency
SI Self-Interference
SIC Successive Interference Cancellation SISO Single Input, Single Output
SLNR Signal Leakage plus Noise Ratio
SNR Signal-to-Noise-Ratio
SOFDMA Scalable Orthogonal Frequency Division Multiple
Access
STR Simultaneous Transmission and Reception
SVD Singular Value Decomposition
TD Tie Domain TDD Time Division Duplexing
TDMA Time Division Multiple Access
TRM Transmit / Receive Module TX Transmit
UE User Equipment
UL Uplink UMTS Universal Mobile Telecommunications Systems
UT User Terminal
WARP Wireless Open-Access Research Platform WCDMA Wideband Code Division Multiple Access
WIMAX Worldwide Interoperability for Microwave Access
3GPP Third Generation Partnership Project 5G 5th Generation
5GNOW 5th Generation Non-Orthogonal Waveforms for
Asynchronous Signalling
II. RELATED WORK
It is probably correct to say that FD system is a new topic of
interest among researchers. However, the principles of full
duplexing in wireless communication have been available since
the 1940s and have been implemented in a range of
communication systems. Currently, there is a rapidly growing
and significant literature on in-band SI mitigation reported in
various surveys. This section presents some survey work
around FD including SI suppression / mitigation as well as SI
cancellation in the earlier days of FD systems.
A. Related Surveys
Some surveys and tutorials have been carried out regarding
FD systems, mainly focusing on the array of technologies that
have been proposed in literature for in-band FD, the evaluation
of the performance capabilities of the FD network, the
challenges for implementing FD systems; including SI, FD
relay systems, opportunities, applications, as well as the
perspective of FD from the physical and MAC layers. The
context of SI as a major challenge for enabling FD operation
have also been either partially mentioned in these surveys or
treated in some reasonable depth. Some of these works are
presented below, with their contributions and research topics
reviewed and presented in Table II.
In [10] the authors review the main concepts of in-band FD
wireless systems by giving an overview of the historical
developments including the research advances in in-band FD
wireless systems. They mirror three basic wireless
communications topologies which could leverage the
opportunities of FD operation. These topologies are the relay
topology, the bidirectional topology and the base station
topology. Recognising SI as the singular biggest practical
impediment to the operation of FD, the survey considers several
techniques for SI reduction while also discussing numerous
research challenges, such as antenna and circuit design as well
as opportunities in the design and analysis of in-band FD
wireless systems. Some of the research opportunities the paper
identifies include: effective channel modelling of the FD
system, optimal resource allocation and optimisation as well as
performance limits for the in-band FD wireless network.
In [23], the authors discuss the importance of FD
communications while highlighting the major drawback - SI. SI
mitigation techniques which covered antenna design techniques
through to digital SI cancellation are provided. The paper
further identifies passive techniques, e.g., antenna separation as
a technique capable of isolating and shielding the reception
from transmission as well as the active techniques, e.g.,
analogue and digital domain SI suppression techniques capable
of SI cancellation in the analogue receive-chain circuitry before
the ADC and SI cancellation after the ADC using signal
processing schemes, respectively. The performance analysis of
FD relay so far carried out as well as assessment of current
developments in FD relaying is presented while discussing a
couple of promising protocols namely: FD Multi-Hop (FDMH)
and FD Block Markov (FDBM), and how they are encoded and
decoded. Whereas the former relies on multi-hopping and seen
as the simplest protocol, the latter is considered as the best
performance achieving FD relaying scheme. It concludes by
discussing the importance of FD relaying on 5G networks.
In [3], the authors examine in-band FD Relaying (FDR) as a
promising technology that shall integrate the advantages of FD
wireless and relaying technology. The paper identifies
interference management, small-size FD device design,
security, cross-layer resource management, channel modelling
and estimation as some of the many challenges and research
issues that need to be addressed before widespread deployment
of FDR can be implemented. In addition to the basics and
enabling technologies of in-band FDR, the paper also presents
SI cancellation in different domains; theoretical information
performance analysis incorporating capacity analysis, outage
probability and diversity-multiplexing trade off; key design
issues including power allocation, antenna selection and
challenges of in-band FDR.
In [5], the authors present three topologies for in-band FD
transmission which includes bi-directional, relay and cellular
topologies. To achieve FD in these topologies they recognise
the need for both passive and active forms of SI management.
The paper evaluated capacities for FD in the given topologies
as well as SI schemes and challenges associated with each
topology. In the same vein, the authors also provide
comprehensive survey of MAC layer issues related to these
three topologies and also present research challenges associated
with MAC protocols for the in-band FD systems including the
need for the development of advanced MAC protocol for ultra-
low latency for 5G networks which needs to be backward
compatible with the traditional HD networks.
In [24], the authors studied specifically the FD
communication in Cognitive Radio Networks (CRNs) – a
deviance from the conventional wireless networks – and
considered SI mitigation in the CRNs. The paper took a narrow
scope and limited its discourse on the architecture, MAC
protocols, spectrum sensing and security requirements for FD-
CRNs only.
Perhaps [25] is the most comprehensive survey around FD
wireless communications. In this paper, the authors review the
state of the art on FD communications highlighting the benefits
of FD communications; investigate critical techniques for SI
cancellation and MAC layer protocols design for FD
communications. The paper also investigates the hardware
imperfections associated with wireless communications as well
as discuss the advantages, disadvantages and design challenges
of an FD system including its applications. More important, the
authors present analysis of passive SI suppressing schemes that
took into cognisance antenna separation, antenna cancellation
and directional passive suppression; analogue SI cancellation
and digital SI cancellation.
Whereas all these surveys have done a good work on
identifying SI as the major drawback to commercial
implementation of FD systems, none of them focused solely on
discussing the SI management of FD systems and the amount
of SI mitigation required to achieve FD communications. We
have bridged this gap by calculating and presenting in Table VI
the minimum required amount of SI cancellation needed to
enable FD for different access schemes from the 1st to the 5th
generation of wireless communication systems. It is important
to note that all the SI mitigation scheme so far studied and
presented discuss only terrestrial wireless networks. As a
further contribution, we study and present SI mitigation
schemes in satellite communication systems. We also present
the modelling aspects of SI signals, especially for multi-antenna
systems which before now, to the best of our knowledge, are
not presented in any of the existing surveys that have studied SI
mitigation.
B. Self-Interference Management in Early Full Duplex
Systems
Radio SI cancellation being the most critical enabler for FD
radios has been an age long technological challenge whose
history transcends over a century [3]. Incidentally, the eventual
success of radio SI cancellation may well depend on not only
improved hardware technology but also innovative signal
processing schemes. From the times, several efforts have been
made in trying to cancel SI in an FD system. Though FD has
not been widespread until recently due to the devastating effects
of SI that a transmitting FD node causes to itself, FD concepts
however have been an old paradigm with a reasonably long
history. Interestingly, researches in this area include those from
radar systems and the traditional telephony systems. FD models
date back to the 1940s with the Continuous Wave (CW) radar
systems [26-27], which uses either shared antenna systems
(mono-static) or separate (bi-static) antenna system for
simultaneous transmission and reception of signals [28]. In a
shared antenna architecture, each transmit and receive chain
pair share a common antenna whereas in a separate antenna
architecture, each transmit-chain as well as each receive-chain
uses a dedicated radiating antenna. Transmitter leakage [29-30]
as SI was termed in those days posed the primary challenge to
the design of CW radars. Isolation in CW radars of the mid-20th
century between the incoming and outgoing signals was
achieved through the use of circulators in the mono-static
antenna systems by exploiting the nonlinear propagation in
magnetic materials [31]. For the bi-static antenna systems,
transmitter to receiver (TX-RX) isolation is achieved through
antenna based path loss. Due to the slight isolation achieved
using these two techniques, keeping SI levels within
satisfactory levels meant intensely limiting transmit power and
consequently limiting the range of these radars to short-range
targets.
Following up to the techniques of the 1940s was an analogue
circuit-based SI canceller known as the feed-through nulling
aimed at increasing the dynamic range of CW radars [29]. This
solution, which was capable of a 60 dB cancellation, came with
a very expensive and heavy leakage canceller which made it
unfeasible. An improved canceller capable of adapting to
varying channel conditions was however proposed in 1990 [20].
This provided further improvement on the mono-static CW
radars by reducing the weight of the leakage canceller and
consequently reducing the price [29-33].
Apart from the CW radars, earliest recorded use of FD in
cellular networks was as repeaters in the 1980s for extending
cellular coverage [29], [32-36]. After then, wireless
communications have not implemented much of FD technology
until recently when the technology have been used
In relay systems where repeaters receive, amplify and re-
transmit signals on the same frequency. Wireless relays are
basically used for boosting coverage in difficult terrain where it
is not cost effective to deploy wire line backhaul technologies.
Just like in the bi-static CW radars, earlier technologies for SI
cancellation in relays employed physical separation of transmit
and receive transmitters [35]. These passive techniques have
recently been taken over by active analogue and digital
techniques [14], [37].
More recently, beamforming-based interference nulling
techniques [13], [34], [38-45], [116] have been made possible
by using antenna arrays. Though rich history exists on enabling
single frequency wireless communication in relays, there has
just been surge in research activities demonstrating the
feasibility of enabling FD communications in other wireless
and cellular systems recently [6], [16], [37], [42-43], [46-48].
TABLE II
SURVEY PAPERS THAT HAVE STUDIED SELF-INTERFERENCE CANCELLATION FOR FULL DUPLEX SYSTEMS
Title of paper Reference Year Contributions and Research topics reviewed
In-band Full duplex Wireless: Challenges and Opportunities
[10] 2014 Presented the research advances in FD wireless communications Mirrors the opportunities presented by FD technology
Discusses the techniques for reducing self-interference
Research challenges and opportunities are discussed
Brief Survey on full-duplex relaying and its applications on
5G
[23] 2015 Notes SI cancellation methods of passive, analogue and digital schemes Discusses FD relaying protocols such as FDMH and FDBM
Analysis the encoding and decoding processes for the protocols
Presents performance analysis of FD schemes FD relaying on 5G is presented
In-band Full Duplex Relaying: A Survey, Research Issues and
Challenges
[3] 2015 Gives a historical perspective of in-band full duplex relaying Classifies full-duplex relaying systems
Discusses self-interference cancellation domains
Presents information-theoretic performance analysis of FDR systems
Points out key design issues and challenges of FDR systems
A survey of in-band Full-
Duplex Transmission: From the perspective of PHY and MAC
layers
[5] 2015 An overview of different in-band FD topologies
Effects of in-band FD on system performance of the FD topologies Brief highlight of passive SI and active SIC
Presents research challenges of the different FD topologies
Full Duplex Wireless
Communications: Challenges, Solutions, and Future Research
Directions
[25]
2016
Benefits of FD operations by making performance comparisons of HD and FD modes
In-depth discussion on self-interference cancellation Presents MAC layer protocol design for FD systems
Implementation, improvement and optimisation issues in FD systems are discussed
Potential future research directions are presented
Some demonstrations reported in [16], [21], [49] using
improved SI cancellation schemes have achieved levels
sufficient enough for enabling FD in WiFi systems, with few
bottlenecks identified. These bottlenecks point to impairments
introduced by the transmit radio chain [49-55]. Whereas
successes have been recorded in some wireless technologies,
the SI cancellation so far achieved even with the proposed
advanced schemes that take into account the transmit radio
chain impairments [7], [11], [46], [56], still fall short of levels
required for the practical implementation of FD system in
wireless networks.
C. Taxonomy of Self-Interference Management in Wireless
Communication Systems
All the works done around the implementation of FD systems
identify SI mitigation as the key to achieving FD
communication. To properly study SI management, we present
a taxonomy as shown in Fig. 2. In doing so, we have identified
four broad classification regimes as explained below:
1) Domain-based classification: Here consideration is made
for the domain under which the cancellation takes place in
classifying the techniques. The domains include: propagation
domain, analogue-circuit domain as well as digital circuit
domain.
2) Non-domain based classification: Under this category, we
consider whether mitigation leads to suppression of SI signals
or cancellation. While the former presents schemes collectively
referred to as passive suppression schemes, the later considers
active cancellation / mitigation schemes. Whereas the active
schemes are further sub-grouped under active and digital
cancellation, some of the techniques criss-cross the several
domains as mention under domain based classification.
3) MIMO-aided classification: With the benefits of MIMO
technology in mind, it is imperative to think of enabling FD for
MIMO systems. To achieve this, solutions for SI cancellation
schemes suitable for the MIMO systems is suggested in
literature. These schemes include natural isolation between the
transmit and receive antennas of a MIMO system antenna array
[15] as well as time-domain cancellation [57] and spatial-
domain suppression schemes [58]
4) Challenges posed by impairments on SI Management: non-
idealities, especially on the RF analogue front-end pose a great
challenge to the SI cancellation capability of FD systems. The
main impairments of concern are the transceiver phase and
quantization noise, I/Q imbalance as well as nonlinearities [5],
[25] which also results in channel estimation errors.
III. SELF-INTERFERENCE CANCELLATION TECHNIQUES
Since FD became a hot research topic in wireless
communications, several attempts have been made at SI
mitigation. Studies are not lacking in SI management
techniques which could be broadly classified using several
indices. Whereas some taxonomical classification consider the
domain under which SI mitigation is performed, others consider
whether SI is passively suppressed or actively cancelled. For
the former, we classify the techniques that make use of a
combination of propagation domain, analogue circuit domain
and digital circuit domain while for the later we consider
passive and active cancellation grouping. In Fig. 3 we show the
various schemes and how they fit under the domain of
cancellation and also present the advantages and disadvantages
of SI cancellation under these domains in Table III.
Classification for passive and active mitigation schemes are
shown in Fig. 4 while a comparison of the performance
capabilities of these schemes is presented in Table IV.
Taxonomy of Self-Interference Management
Propagation domain
Domain basedNon-domain
basedMIMO based Impairments
Passive Suppression
Precoding-based Isolation Phase Noise
Analog circuit domain
Digital circuit domain
Active analog cancellation
Active Digital Cancellation
Spatial -domain mitigation
Time-domain mitigation
I/Q Imbalance
Nonlinearity
Fig. 2. Taxonomy of Self-Interference Management
A. Classification of Self-Interference Management Schemes
based on Domain of Cancellation
1) Propagation Domain Self-Interference Management
Schemes: Propagation-domain SI cancellation schemes seek to
separate the transmit chain and the receive chain using
electromagnetic properties. It achieves this by suppressing the
SI before it shows up in the receive chain circuitry. It is
accomplished mostly by a combination of passive schemes
including antenna directionality [18], [36], cross-polarisation
[10], [37], [42], [59], path loss resulting from antenna
separation [6], [16], [29], [37], [47], [58] and an active scheme
in the form of transmit beamforming [3], [10], [60]. The
propagation-domain SI suppression suffers from the possible
problem of having the desired signals also suppressed in the
process of trying to suppress the SI [10]. This is because the SI
at the receiver of an FD terminal is usually very high and could
easily exceed the capability of the receiver circuitry thus
overwhelming it in the process.
2) Analogue Circuit-Domain Self-Interference Management
Schemes: In the analogue-circuit domain SI cancellation
schemes, a copy of the transmitted signal is tapped from the
transmitter and subtracted from the receive feed after
appropriate delay, phase and gain adjustments have been made.
This tapped signal from the transmitter could be described as
auxiliary transmit signal whereas the transmitted signal is
described as the primary transmit signal. The auxiliary transmit
signal is pre-filtered by adding pre-weighting coefficients to the
Orthogonal Frequency-Division Multiplexing (OFDM) tones
and Radio Frequency (RF) modulated [12] then added to the
received signal in the RF domain before the Low Noise
Amplifier (LNA) or it can be added in the analogue baseband
before the Analogue-to-Digital Converter (ADC). These
schemes aim to suppress SI just before the ADC within the
analogue-receive chain circuitry by using both adaptive (e.g.,
Balun) and non-adaptive (e.g., QHx220 chip) SI cancellers. The
schemes within the analogue-domain circuitry are however
affected by environmental factors such as reflections and
refractions which could not be predicted and modelled in the
design stage. Again, Channel State Information (CSI) cannot be
exploited in the analogue domain making it practically
impossible to implement a dynamic scheme in this domain. For
these imperfections and challenges obtainable within the
analogue circuitry at present, cancellation in digital domain
becomes imperative.
3) Digital-Domain Self-Interference Management Schemes:
Digital-domain SI mitigation takes place after the ADC by
means of Digital Signal Processing (DSP) techniques applied
on the receive signal. In the digital domain, CSI can be
exploited and used in SI cancellation and also in resource
allocation for learning and determining, for instance, the
appropriate power allocation required across the network
resources (time, frequency, bandwidth) [20], [36], [43].
However the ADC limits the dynamic range of the amount of
SI that can be mitigated in the digital domain circuitry [10]. The
DSP that goes along with digital domain SIC can also be a
complex and costly process.
B. Passive Suppression Schemes
Passive SI mitigation schemes rely on separating the transmit
RF chain from the receive RF chain. There have been proposals
for passive cancellation techniques which rely on antenna
directivity in combination with physical separation of the
antennas, polarisation and use of additional RF absorbing
materials [37], [61]. When each of these techniques are
employed either as standalone solution or in conjunction with
one or two other passive techniques, the primary idea is
isolating the transmit RF chain from the receive RF chain as
much as is possible. We present below the passive suppression
schemes available in literature for SI mitigation.
1) Antenna separation: is the most common technique
for achieving SI suppression at antenna level. This technique
requires large distances between antennas for sufficient SI
TABLE III DOMAIN-BASED SIC SCHEMES CLASSIFICATION: ADVANTAGES AND DISADVANTAGES
SIC DOMAIN Advantages Disadvantages
Propagation Domain [3], [6-7], [65], [14-20]
SI mitigation due to path loss
Capable of improving power efficiency
More separation results to more SI attenuation
Reduces inter-device interference
Ease of implementation
Capable of high cancellation
Robust in narrowband systems
Unfeasible for small form-factor devices
Requires large separation distance
Suffers channel degradation
Analogue-Circuit Domain [3], [7], [16], [65], [66]
Easy implementation
Low complexity compared to digital domain schemes
Compensates for multipath propagation
Enables advanced optimization
Minimises power of residual SI
Improves the useful signal
Suppresses both SI and noise
Adapts to varying Signal to Interference Ratios
Suitable for wideband frequency flat channel
Uses off-the-shelf MIMO radios
Designed only for flat fading channels
Requires self-interference estimation
Requires CSI at the base station
Channel attenuation impacts performance greatly
High complexity compared to propagation
analogue domain schemes
Extra hardware cost
Digital-Circuit Domain [3],
[14-20], [34], [65] Eliminates residual SI following analogue
cancellation
Modulation independent
Has high collision combating capability
Address hidden node issues
Increases quantization noise
Limited cancellation capability
Might not be required after a good analogue
cancellation
Classification of self-interference cancellation schemes
Propagation domain Analogue circuit domain Digital circuit domain
Cross polarization
Antenna separation
Directional antenna
Transmit beamforming
Adaptive SI canceller
Non-adaptive SI canceller
Receive beamforming
Digital SI canceller
Duplexer Path loss
Fig. 3. Classification of self-interference cancellation schemes.
suppression. It employs the idea of increasing the pathloss
between transmit and receive antennas and exploiting
surrounding obstacles (e.g., buildings and shielding plates) in
blocking direct paths [3], [62]. This approach has been applied
in traditional in-band repeaters [37], [63-64] to suppress SI by
increasing the physical distance of separation between transmit
and receive antennas. Antenna separation technique has also
been used in some recent test beds [7], [16], [58].
2) Antenna placement is a cancellation technique
(sometimes classified as analogue-domain scheme [68]) based
on antenna placement [6], [9], reported in [6] as antenna
cancellation. This technique as reported in literature [5], [7],
[68], involves two transmit antennas spaced apart with the
receiving antenna placed in between at distances d and d+λ/2,
respectively so that the transmit antennas are able to
superimpose a null at the receive antenna (λ is the wavelength
of the operational frequency) and hence cancel each other at the
receive antenna. The technique uses the fact that the distance
between transmit and receive antennas naturally reduces the SI
due to signal attenuation. The cancellation is achieved by means
of phase offset. In a simple implementation scenario, the
transmission signal is split between two transmit antennas
sandwiching a receive antenna as described above resulting in
destructive and constructive interference patterns over space
[6], [11]. Antenna placement suffers bandwidth constraints due
to large range of signal wavelength [6], [9]. Employing separate
transmit and receive antennas has potential for better SI
suppression but this multi-antenna system comes at a cost to the
spatial domain which includes degrading the antenna radiation
pattern and spoiling the far field coverage. For example, the
three antenna architecture of one receive antenna and two
transmit antennas with 180 degrees phase shift proposed in [7]
causes transmit signals to add constructively while cancelling
out the receiver. Again, for this technique to work, the distance
between antennas must be large enough in order to achieve an
acceptable value of cancellation. This is not always possible,
especially given the small form factor of compact radio nature
(Table V shows some reference form factor values for some FD
devices ) of most wireless communication devices such as
Smart phones, Netbook, femto cells, etc.
Antenna placement is useful especially for narrowband cases
but would suffer SI cancellation performance degradation in
cases of wideband signals where null regions of destructive
interference is created in the far-field region in effect destroying
the far field coverage [5], unless used for larger form-factors
where system size constraints may be limited. The technique
does not adapt to environment conditions as it requires manual
tuning and needs three antennas which represents extra cost for
hardware. It could also suffer severe amplitude mismatch
between the two transmit antennas. This is because the
technique work for antennas optimally positioned only in the
line-of-sight (LOS). If antennas are off LOS the reflected
signals may not cancel out, thereby limiting the capability of
antenna placement. The technique is only capable of 60 dB
cancellation [6]. However, pair-wise symmetric antenna
technique [14] can help overcome the bandwidth constraints
inherent in the antenna placement procedure. Pair-wise
symmetric antennas theoretically have zero coupling over entire
frequency range. This implies that symmetrical transmit and
receive antennas can be positioned in such a way that SI is
reduced. The cancellation is achieved by means of phase offset,
and has been classified by some authors as an active SI
cancellation technique.
3) Cross-polarisation is another passive SI mitigation
technique that electromagnetically increases the isolation
between transmit and receive antennas [3]. Polarisation of an
antenna dictates and determines the direction and sense of the
electric field vector radiated by the antenna. Ideally, the energy
radiated between two orthogonal polarisations is zero indicating
that maximum energy will occur between two antennas if their
polarisations are the same but will reduce when there is
polarisation mismatch. When the transmit signal of an FD node
is horizontally polarised for instance, it can only receive
vertically polarized signals with the aim of avoiding
interference between the antennas. Cross-polarisation can be
applied to both separate antenna systems [69] and shared
antenna systems [70]. Shared antenna deployment uses less
space. The technique is promising for small form-factor device
deployment and is recently gaining prominence following the
work of D. Bhardia et al., [7] which have shown the feasibility
of deploying shared antenna systems in a Single Input Single
Output (SISO) scenario. In this scenario, isolation is achieved
within the shared antenna system by the means of duplexers [7],
[14], [71]. FD antennas stand to benefit by utilizing orthogonal
polarisations in order to increase antenna energy isolation.
However, as effective as the current passive SI management
schemes are in mitigating SI resulting from direct paths, it is
bedeviled with some problems. Some of these include not being
feasible for small-form-factor devices [7], [61], and being
adversely limited by environmental factors since the techniques
are unaware of the system characteristics and do not take them
into account. There is also a possibility of inadvertently
suppressing the desired signal while trying to adjust transmit
and receive patterns in passive SI mitigation [10], [61]. For
instance, some impractical antenna separation distances can
actually thin out the desired signals. Similar situation is also
possible if the angular separation using antenna directionality is
totally out of phase or not implemented correctly.
C. Active Self-Interference Cancellation Schemes
Most active cancellation schemes are done in the active
analogue circuit-domain as described in Section III-A. Active
cancellation techniques use active components and exploit the
knowledge of a node’s own SI Signal in generating a
cancellation signal that can be subtracted from the received
signal [26], [61], [70]. The family of active mitigation
techniques can be subdivided into active analogue cancellation
and active digital cancellation and mixed active analogue /
active digital techniques [62]. The active cancellation method
employed before the digitization of the received signal is called
active analogue cancellation whereas the active cancellation
methods employed to cancel the residual SI within the received
signal after digitization is called digital cancellation [16], [40],
[72-74].
1) Active Analogue Cancellation Schemes: Analogue cancellation schemes generally cancel SI in the
analogue-receive chain / circuitry by subtracting a copy of the
predicted SI from the received signal before it enters the digital
circuit, just before the ADC. As already stated, the scheme
involves mirroring the primary transmit signal and obtaining
the auxiliary transmit signal which is pre-weighted and RF
modulated before adding it to the receive signal just before the
ADC. These schemes can be classified as either adaptive or
non-adaptive depending on their abilities to respond to
changing effects of the environment [46], [60], and [71].
Active mitigation schemes
Passive mitigation schemes
Active digital cancellation
Active digital cancellation
Active analogue cancellation
Antenna separation
Cross-polarization
Antenna placement
Antenna directionality
Transmit beamforming
Tunable network
QHx220 chip
ADC with analog
ADC with analog
Use of Balun
ADC without analog
Non Domain-based classification of SIC schemes
Transmit-Receive Module
On-board Relay
Double Talk
Fig. 4. Self-interference cancellation schemes.
TABLE IV
COMPARISON OF DIFFERENT SELF-INTERFERENCE CANCELLATION SCHEMES
Schemes Techniques Capability Pros Cons References
Passive
Suppression
Antenna directionality 30 dB Easy to implement
Provides directional diversity
Suitable in narrowband scenarios
Suffers bandwidth constraints due to
large range of wavelengths
Not suitable for wideband
[66], [74]
Antenna placement 47 dB Robust in narrowband scenarios Requires 3 antennas; extra cost Suffers severe amplitude mismatch
Requires manual tuning and so do
not adapt to the environment
[6], [14], [47], [75]
Antenna separation 30 dB Uplink and downlink are spatially
separated Uses the idea of increasing path loss
between TX and RX antennas Ease of implementation
Not applicable to point-to-point
scenarios where both nodes are FD enabled
Not feasible with small form-factor devices
Suffers degradation on individual
antennas radiation pattern
Can suppress desired signal
[3], [47],
[54], [72]
Cross-polarisation 50 dB Can be applied to both separate and shared antennas
Can be applied to small form-factor
devices with duplexers
Unaware of system characteristics Affected by environmental factors
[7], [19], [62], [74]
Active Suppression
Analogue Cancellation
Balun circuit
45 dB Generates inverted version of the Received signal for cancellation
Non-bandwidth limited; non-power
limited Adaptive to the environment
Incurs additional non-linearity from the noise cancelling circuit
Cancellation is not adequate
[46], [60], [74],
Electric Balance
Duplexer
Highly Frequency
dependent
Uses one antenna Suitable for small form-factor devices
Tunable over a wide frequency range
Not constrained by separation distance
Frequency dependent Requires manual tuning
Not very good power handling
capability Prone to non-linear IB distortions
Limits isolation bandwidth
[78-80]
QHx220
chip
45 dB Provides extra RF chain Non-adaptive to the environment
Difficulty in wideband scenarios
[15-16],
[32], [74]
Digital
Cancellation
With
analogue cancellation
60 dB Suppresses both SI and noise Suffers transmitter distortion due to
non-ideality of transmitter and receiver components
[7], [14]
Without
analogue
cancellation
10 dB Capable of eliminating residual SI
after analogue cancellation
Hardware impairments including I/Q
imbalance
[6], [15-
16], [37]
TABLE V REFERENCE FORM FACTOR VALUES FOR FULL DUPLEX DEVICES [68]
Full Duplex
Devices
Access Point Type Form Factor
Dimensions
Base Stations Femto Base Station 236 x 160 x 76 mm
Pico Base Station 426 x 336 x 128 mm
TETRA Base Station 55 x 143 x 57 mm
User Equipment Netbook 285 x 202 x 27.4 mm
Tablet PC 241.2 x 185.7 x 8.8 mm
Smart Phone 123.8 x 58.6 x 7.6 mm
PDA 132 x 66 x 23 mm
The adaptive active analogue cancellation techniques
dynamically adjust their parameters according to the reflected
channel and are able to mitigate both direct-path and reflected
SI signals. An example of the adaptive analogue scheme is the
use of RF Balun. This scheme uses signal inversion technique
for SI mitigation [46]. The mechanism employed by the active
analogue cancellation circuitry is for generating a cancellation
signal which simply makes use of balanced-to-unbalanced
converter to generate the inverse of the SI signal which it uses
to cancel the SI. The SI generated by the transmitting node is
met with the slightly modified (modified by applying a variable
delay and attenuation) inverted SI (auxiliary transmit) signal
applied by the means of a noise cancelling integrated circuit.
This scheme involves tuning algorithm which controls the gain
and delay that the chip applies to input. The Balun cancellation
technique is theoretically capable of providing SI cancellation
of 45 dB and has no power or bandwidth limitations [6], [65].
However, on the flip side, its capabilities still fall short of the
required SI cancellation to enable FD. It also incurs additional
nonlinearities from the imperfections inherent with the RF
Balun and the noise cancelling circuit.
The non-adaptive schemes are not environment aware. They
are sensitive to reflected paths of SI because they are not aware
of the changes in the environments [15-16], [78], [81]. They
require manual tuning and use fixed parameters such as gain,
delay and phase in predicting SI. An example is the use of the
noise canceller chip, QHx220 as depicted in Fig. 5a. QHx220
chip is used to introduce an extra circuit. This circuit generates
an extra RF suppressing signal which is subtracted from the
receivers’ chain a known analogue SI signal from the received
signal [65]. The idea in using an extra transmit RF chain is to
estimate channel and design an extra inverted RF chain by
means of off-the-shelf radios which adds to the transmit signal
at the receive chain as shown in Fig. 5b.
The DUPLO project also proposed another non-adaptive
active analogue SI cancellation scheme that uses a tunable
cancellation network combined with a dual-polarised antenna
[62] as depicted in Fig. 6. It has similar working operation to
the RF Balun set up. The network is designed to copy a portion
of the transmit signal with the in-band impairments using a
directional coupler which it adds back to the receive path before
the LNA after phase rotation and attenuation. The main
advantage of performing the active cancellation close to the
antenna ports is that transmitter impairments are maximally
included in the cancellation loop, thereby improving the
cancellation and reducing the potential of saturating the first
receiver components. After the modification and 180 degrees
phase-shifting (inversion as in Balun), the addition is achieved
by means of an RF combiner. This addition is made before the
LNA because the losses added to the network at that stage are
very minimal. A result of simulation done with the active
cancellation network using discrete components and the dual-
polarised antenna shows an SI cancellation of 15 dB over 10
MHz [70], [81].
DAC
ADC Rx RF
Tx RF
RX chain
TX chain
+
DAC Tx RFExtra
TX chain
g
DAC
ADC Rx RF
Tx RF
RX chain
TX chain
+
DACRF generating
chip
g
QHx220
(a) QHx220 circuit (b) Extra TX Chain from QHx220 Circuit
Fig. 5. The non-adaptive extra RF chain circuitry for SIC [37]
Antenna
Coupler
Detector
Variable Amplitude
CouplerVariable Phase Combiner
From HPA
Tx port Rx port
To LNA
Fig. 6. Tunable analogue cancellation network with dual-polarized antenna
[70]
Furthermore, the tunable cancellation network described
above is not so different from the Electrical Balance Duplexer
(EBD) [80]. A hybrid transformer-based EBD provides both
Differential Mode (DM) and Common Mode (CM) signals at
the receiver input. The DM is to provide SI reduction and
protect the receiver from CM signals which are capable of
causing breakdown of the LNA due to large voltage swing at
the input of the LNA. EBD demonstrates the usage of a
conventional single-port antenna in achieving FD operations by
exploiting the electrical balance in hybrid junctions to provide
high TX-RX isolation over wide bandwidth [78]. The concept
of EBD is the balancing of the antenna impedance with
balanced network impedance at ports of a hybrid junction. An
EBD can pass signals between the transmitter and the receiver
of the antenna while also providing cancellation of SI
originating from the local transmitter at the same frequency.
Using multiple antennas as is obtainable with antenna
separation and antenna placement introduces null in the antenna
beam pattern which in turn degrades coverage. The strength of
EBD which is capable of providing up to 60 dB of isolation at
20 MHz bandwidth, 50 dB at 40 MHz and 30 dB at 200 MHz
[80] lies in its suitability for implementation in small form-
factor devices because of its usage of a single antenna while
ensuring good far-field coverage. Unlike antenna separation,
the capability of the EBD is not limited by physical separation.
It is tunable over wide range of frequencies and can be
implemented using integrated circuits. However, the TX-RX
isolation can only be obtained when the balancing impedance
closely matches the antenna impedance. This limits the
isolation bandwidth. Again, EBD is susceptible to in-band
distortions produced by nonlinear components of the duplexer
causing difficulties in subsequent SI cancellation in the
transceiver.
Similarly, [7] and [16] reported an experiment set up that
used Wireless Open-Access Research Platform (WARP) in
providing an extra transmit chain which generates an inverted
cancellation signal that was subtracted from the receive chain.
The primary difference with this scheme and the Balun is that
whereas this technique performs SI cancellation by phase
offset, the Balun performs SI cancellation by signal inversion.
Not so long ago, Stanford university researchers [7], reported
impressive results showing FD operations based on a SI
cancellation technique that utilises a 10x10 cm Printed Circuit
Board (PCB) circulator equipped with several adjustable
attenuators, each of varying length capable of introducing
varying delays. The design was made to reproduce the antenna
mismatch reflection and the circulator leakage. Though there
are promising results with the work as reported, especially SI
cancellation up to WiFi noise floor of about 110 dB, some other
issues with real life application including nearby obstacles are
yet unresolved. Moreover, their prototype is frequency
dependent and targeted WiFi frequencies in the 2.4 GHz band.
It also targets SISO scenarios making the cross talk you get with
MIMO systems still an issue of concern and thus open for
further investigations. The 10x10 cm prototype board is suitable
for mid-sized form-factor devices and the technique might not
be applicable to larger cells and very small portable devices.
2) Active Digital Cancellation Schemes: Current mitigation schemes presented by the passive and
analogue circuit technologies have shown impressive promise.
Nonetheless, though they may be able to provide enough SI
cancellation for repeaters (relay systems) [3] which only
receive-amplify and forward, they do not present sufficient
suppression for some other cases and scenarios, for instance
cellular networks which need to also decode the receive signals.
For this reason, active digital cancellation technologies become
handy. It is identified in Literature that Digital SI Canceller
(DSC) and transmit beamforming are schemes which could be
adopted in digital domain to actively suppress residual SI which
might have escaped the passive and analogue circuitry. Digital
cancellation makes use of complex and advance DSP
techniques in mitigating SI. The DSC estimates the residual SI
after passive and analogue cancellation and subtracts this
predicted or estimated signal from the received baseband
samples in digital domain [15-16], [37]. In the receive
beamforming technique [18], a MIMO scenario is considered
in which case SI is suppressed by adaptively adjusting per-
antenna weight according to the SI channel condition. Receive
beamforming can also be implemented in analogue domain, but
is commonly implemented in digital domain owing to
complexity and power consumption issues.
Digital cancellation suffers from transmitter distortion due to
non-ideality of amplifiers, oscillators, ADCs and DACs. The
active digital cancellation techniques would be successful only
if an equivalent discrete time baseband model that is able to
capture all the distortions of the FD terminals is built. To solve
this problem, [7] proposed and experimentally demonstrated a
hybrid joint analogue-digital design capable of modelling all
linear, nonlinear and transmitter noise distortions and
cancelling SI up to the noise floor. Their work is credited with
presenting the first realistic demonstration of using a
combination of SI mitigation schemes in realising FD. Though
the size of the board used for implementing the circulator fits
well with BSs for cellular networks, the work was implemented
on the 2.4GHz band. Besides, the components of the circuit are
frequency dependent, making it unadaptable to varying
frequency scenarios. Besides, the targeted scenarios were SISO
leaving a huge challenge for implementing the solution on
MIMO where cross-talk between antenna elements presents a
challenge.
With these challenges in mind, coupled with the complexities
of the active cancellation schemes, the expectations are that
there is need for further research into more cost effective, less
complex solutions capable of modelling not just signals from a
single antenna but also the distortions from multiple antennas.
We discuss impact of impairments on SI cancellation in the
following sections while possibilities for overcoming these
challenges, especially for the MIMO scenarios are presented
under the section for open research questions and future
directions.
D. Self-Interference Mitigation Schemes for SatCom
Historically, Satellite Communication (SatCom) has always
served as an effective medium of coverage infrastructure
mainly in areas that are not adequately covered by the terrestrial
communication infrastructure. Though several technologies
have been suggested and implemented to improve spectral
efficiency in SatCom such as reducing the guard bands with
improved waveforms, however the availability of satellite
spectrum has always been a big challenge. Just like cost is of
big concern to terrestrial wireless systems, high cost of multiple
transponders on-board a satellite fuels the interest in FD for
SatCom. If FD is achieved in this environment, it will save
money and increase the number of users that can be served
within a given RF #band [81]. Achieving this though is still a
significant challenge given the large distance and large power
transmission required for SatCom. Whereas FD communication
has made a significant head way in terrestrial communication,
owing to its low power transmissions and short distances, its
application to SatCom is still in its early days. The difficulty in
implementing an FD system increases with the distance
between the radios; the larger the difference between the TX
and RX power, the more challenging the problem becomes [83].
In SatCom, the distance of separation between the TX and RX
antennas are enormous when compared with terrestrial
networks. In the following subsections, we present the
techniques for SI mitigation in satellite systems when enabling
FD communication.
1) On-board FD Relay System: Fig.7 describes a satellite
system operating in the FD mode. This technique involves the
use of same frequency band on the feeder uplink represented by
the Gateway (GW) to Satellite link and the downlink
represented by the User Terminal (UT) to satellite link. To
access the feasibility of the solution, [84] carried out a rigorous
analysis of the impact of SI. In their feasibility work, they
demonstrated the use of FD relaying principles on-board the
satellite systems, and show, from a technical point of view, that
satellite FD communication can be a promising solution for the
efficient use of satellite spectrum. First, the authors identified
different sources of interference on the on-board relay system
to include: high power amplifier (HPA) non-linearities,
memory effects and on-board noise components. The SI
comprises of the linear and the non-linear terms and is induced
by the SI channel; the non-linear components of SI, are due to
the transponder characteristic and can be modelled as a non-
linear function without memory. The noise component is
broken down into the Uplink noise component (which is
generated by the transponder and unaffected by the SI), the
Downlink and Receiver noise (due to the transponder and the
UT), and the Full-Duplexing noise (which arises as a result of
the SI). The SI component and transmit noise are mitigated by
non-adaptive analogue cancellation by tapping a line from the
transmit antenna to the receive antenna.
However, there remains the challenge of accurately
estimating the on-board SI channel. Whereas, as earlier
mentioned, existing works have discussed the techniques for
mitigating these impairments in terrestrial communication via a
mix of analogue and digital techniques, for the on-board relay
system, only RF cancellation can be implemented. Lack of
processing capabilities impede the on-board digital
cancellation. Furthermore, on-ground predistortion and
equalisation at the GW and UT, respectively, can be introduced
to augment the on-board analogue interference mitigation [84-
85].
User Terminal
SI Channel
Gateway Fig.7. SatCom Full-duplex forward path relaying showing SI [84]
2) Double Talk: Double Talk is a kind of an echo canceller for
satellite signals [82]. Each end of the link sees a modified
“echo” of its own transmitted signal in the downlink it receives.
Whereas the concept of using echo cancellation in terrestrial
networks is simple, to meet the requirements for using it in
challenging satellite environments require significant
enhancements. These enhancements are required because:
Non-static frequency offsets are imposed by the uplink
and downlink conversions and the frequency
translation of the transponder.
A Doppler shift due to satellite motion is present as the
motion of the satellite(specifically for low and
medium earth orbit satellites) causes the round trip
delay between the earth stations to be time varying.
Non-Linearities in the RF electronics throughout the
processing cause distortion and sometimes introduce
interference between carriers.
Each of these effects has to be compensated in the canceller
architecture. In [82], the authors were able to use digital signal
processing (DSP) to address the enhancement requirements by
utilizing the concept of an echo canceller for satellite signals,
and its use to eliminate intentional echoes of signals introduced
solely for the purpose of minimising the link bandwidth.
Double Talk was originally intended to be modem agnostic and
had only IF interfaces. A high level signal processing
architecture of Double Talk is shown in Fig. 8, where;
Rx In denotes the received composite IF downlink
signal consisting of both the desired signal from the far
end of the link and the (co-channel) echo of the
transmitted signal (Tx Out) from the local modem.
Tx In denotes the IF uplink signal from the local
modem.
Rx Out denotes the IF output signal from DoubleTalk.
This is the signal from the far end of the link recovered
in Double Talk by cancelling echo of the local uplink
signal Tx Out.
Rx Out is subsequently passed to a suitable modem for
demodulation.
IF to baseband Interface
IF to Baseband Interface
Delay Controller
Coarse Delay
Ad
ap
tiv
e F
IR
Fil
ter
Err
or
Ge
n
an
d W
eig
ht
Up
da
te
+
+Rx In
Tx In
Rx Out
Tx Out
Fig. 8. Block Diagram showing the Double Talk Implementation [82]
The essence of DoubleTalk is quite simple. The local IF
uplink signal is digitised, stored in memory, adaptively filtered
and subtracted from the received input. An adaptive FIR filter
(canceller) using minimum output power (MOP) or least mean
square criterion is used to adjust the filter taps to minimise the
differences between the local reference and the downlink
signals [86-87]. Because the signal from the far end of the link
is presumed uncorrelated with the locally transmitted uplink
signal, the MOP criterion is satisfied only when the desired
signal from the far end of the link remains after the cancellation
process. This is a classic ‘noise cancelling’ adaptive filtering
problem. The performance of this scheme in the lab yielded 28-
29 dB cancellation, which is far from the supposed 130 dB [82]
required to enable FD operations in the SatCom systems.
3) Transmit / Receive Module (TRM): The authors in [88]
study a FD, multi-channel TRM for an S-Band SatCom Phased
Array system. The multi-channel S-Band TRM has been
designed for SatCom on FD mode, and applicable to satellites
in Low Earth Orbit (LEO), Medium Earth Orbit (MEO) and
Geo Synchronous Orbit (GEO). The TRM utilises over 90
Monolithic Microwave Integrated Circuits (MMICs) for
transmit and receive beamforming and array control and has
internal shields in the modules which provide RF isolation with
the array providing simultaneous multiple high gain transmit
and receive beams within a hemisphere to communicate with
satellites during the time they are above the local horizon. The
main design components of the TRM, consists of high rejection
ceramic diplexers, low noise MMIC amplifiers, 4- Bit transmit
and receive digital phase shifters and integrated circuit micro
controllers. Included in the design are polarization diversity, RF
shielding and design considerations for interfacing with a
beamformer, among other things. Whereas this solution could
be optimised to offer a reasonable solution for the FD SatComs
by building a plug and play device within the SatCom systems
capable of a 45 dB cancellation, the drawback is that there is a
difference in the transmit and receive frequency bands which
are 1.75-2.1 GHz and 2.2-2,3 GHz, respectively. This do not
necessarily imply STR on same radio resource. It should be
interesting researching more on the possibility of implementing
the TRM on same frequency band.
IV. EFFECTS OF TRANSCEIVER IMPAIRMENTS ON SELF-
INTERFERENCE MANAGEMENT
It has been suggested in literature that active SI cancellation
is capable of ridding the FD system of issues of SI and bringing
home the benefits of FD systems. However, several non-
ideality issues limit the performance of the SI mitigation. Some
of these includes receiver noise, transmitter noise, phase noise,
channel estimation errors, dispersion and nonlinearities. As the
prospects for the implementation of FD systems increase with
improvements in the SI mitigation techniques, it is imperative
to understand the dominant nonlinearities within the system and
correctly model them. With the increasing complexities of
wireless communication systems, nonlinear behaviours are
observed in more and more blocks. These nonlinear behaviours
as a result of hardware imperfections pose the highest limiting
factors to conventional SI mitigation techniques [5]. This is
especially true in FD systems where due to strong SI signals,
the system nonlinearities pose huge limitations to the
cancellation of the SI power. Radio elements in a wireless
communication systems are susceptible to RF front-end
impairments. These become more obvious with higher-data rate
devices such as FD radios both in the RF analogue front-end
and digital baseband. For instance, as shown by [89], as a
requirement for active cancellation the active analogue
canceller needs to mitigate the analogue SI to ensure it meets
the Analogue-to-Digital Converter (ADC) sampling
requirement. However, the active analogue cancellation
performance is often restricted by the non-ideal electronic
components, e.g., the tunable attenuator, the phase shifter and
associated circuits, which introduce nonlinear distortions to the
residual SI signal that enters the receive chain, as the transmit
power increases.
To tackle the key challenge of SI in FD systems, suggestions
have been made for joint analogue and digital cancellation
which take into account the characteristics of the RF analogue
imperfections. Whereas it is not strictly accurate to state that the
total amount of SI cancellation is directly proportional to the
amount of analogue cancellation, it is however correct to say
that the amount of digital cancellation achieved has a direct
bearing to the amount of analogue cancellation when cascaded
[25]. In other words, if the analogue cancellation reduces the
system SI to a lesser degree, then digital cancellation is able to
cancel the residual SI even more substantially.
In practice, SI consists of multiple components as the
transmit signal is corrupted by these different impairments,
such as nonlinearity, phase and quantization noise [90]. Some
of these by-products are noisy, others are deterministic. The
transmit signal, including its by-products, is coupled into the
receiver through various paths, e.g., direct crosstalk, TX-RX
antenna leakage due to limited isolation, and reflections on
nearby objects in the environment. To achieve a receiver
sensitivity similar to the conventional HD radios is very
challenging, as all SI components should be suppressed to
below the receiver noise floor. SI cancellation in the analogue
circuit is still limited by a number of impairments which for the
sake of this survey, we have classified as: transceiver phase
noise [74], quadrature imbalance, and power amplifier
nonlinearity [72], [77]. There are however other non-idealities
which impact the SI cancellation capabilities in FD systems.
These includes: ADC quantization noise [91], carrier and
frequency offsets etc. Both the transmitter and the receiver are
impacted by impairments with both consisting of nonlinearities
and noise components as well as some system level
impairments.
A. Transceiver Phase noise
The authors in [74] note that the amount of active analogue
cancellation is limited to 35 dB. This is as a result of phase noise
in the local oscillator which limits the amount of active
cancellation [92]. Phase noise in the transceiver causes the
disturbances which ensures the SI and the nulling signals do not
cancel out. This claim is further elucidated by the analysis in
[15] which demonstrated that the transceiver oscillator phase
noise is one of the major bottlenecks limiting the amount of SI
cancellation in practical FD systems. In [16], it was analytically
established that the capacity gain of FD systems is significantly
decreased as phase noise increases / becomes stronger making
it clear that for efficient SI cancellation, reduction of transceiver
phase noise should be considered seriously. In [74], the authors
present an analysis of the impact of phase noise on the strength
of the residual SI signal on analogue cancellation. In the
analogue domain, an imperfect SI channel estimation was
considered with the conclusion that the residual SI signal
strength consists of the SI-dependent component as well as the
phase noise-dependent component in the pre-mixer, post-mixer
and the baseband canceller. It could be inferred from their
studies that in the presence of high received SI power levels,
phase noise will dominate the residual SI after analogue
cancellation because the phase noise dependent component
scales linearly with the SI power [25].
B. In-Phase /Quadrature Imbalance
A signal from the transmitter is usually received in the
receiver as a modulated signal. A modulated signal includes an
in-phase component and a quadrature component. There is
always an amount of deviation in the proper alignment of the
in-phase and quadrature components of the modulated received
signal. This deviation may occur in both the amplitude and the
phase of the in-phase and quadrature components of the signal.
Even though other system impairments such as phase noise and
sampling jitter degrades the SI cancellation ability of several
techniques [93] describes I/Q imbalance and PA nonlinearities
as the most prominent impairments that limit the system
performance especially the precision of digital SI cancellation
techniques. On the transmitter side in general, I/Q imbalance
contributes to the transmitter error vector magnitude (EVM)
and also adjacent channel leakage [94]. It represents an
additional loopback signal leakage to the receiver path. It is
noted in [73], which studied the effect of I/Q imbalance on the
FD transceiver, that IQ imbalance causes residual SI even after
all the cancellation stages. The discrepancies of I/Q imbalance
parameters within two transmission chains causes the
generation of imprecise SI signal. This hampers the
performance of SI suppression. To study the influence and
effects of I/Q imbalance in the performance of SI mitigation in
FD devices, and how to mitigate I/Q imbalance in a wireless
transceiver, [95] proposed advance pre-equalisation units which
are able to handle the I/Q impairments. Though the results of
the analysis presented are appreciable, they more than anything,
showed the dire effects of the I/Q imbalance to the SI capability
of an FD system.
C. Transceiver and Power Amplifier Nonlinearities
Generally, RF / analogue impairments cause signals in
wireless communication systems to be distorted in different
subsystems. These distortions are made up of the linear
components as well as the nonlinear components [72]. As [89]
notes, the main sources of the system nonlinearities in practical
systems are the power amplifier at the transmitter side and the
LNA at the receiver side. These pose a significant challenge to
the SI cancellation ability of the FD wireless system. According
to [7], [89], [97] the nonlinear distortions in an FD transceiver
can be approximated using polynomials. Several works have
modelled nonlinearities in an FD device, so we shall not be
making any analytical derivations but shall refer to a couple of
such modelling.
The output, y of any wireless system nonlinear component can
generally be modelled as in [92] as a polynomial function of the
input signal as follows:
𝑦(𝑡) = ∑ 𝛽𝑝Γ(𝑡)𝑝
𝑃
𝑝=1
(1)
Where, the first term Γ(𝑡) , represents the linear component
input, the higher order terms contribute to the spurious
nonlinear component. This is consistent with the analysis of
[97] which showed that the 2nd and 3rd order terms or receive
chain induced nonlinearities are the most significant distortion
components with transmit powers above 10 dBm. Furthermore
[92] shows that only the odd orders of the polynomial
contribute to the in-band distortion. Accordingly, equation (1)
can be simplified and written in the digital baseband as:
𝑦𝑞 = ∑ 𝛽𝑝Γq|Γq|𝑚−1
𝑃
𝑝=1
(2)
Where, Γq and 𝑦𝑞 are the digital base-band representation of
the input and the output of the nonlinear component and P is
odd.
To model the nonlinear distortion of the active analogue
circuit, the Memory Polynomial (MP) model with even order
nonlinear terms is used. The MP model can be described as
[98]:
𝑦𝐴𝐶[𝑛] = ∑ ∑ 𝑤𝑘𝑞𝑥[𝑛 − 𝑞]|𝑥[𝑛 − 𝑘]|𝑘
𝑄−1
𝑞=0
𝐾−1
𝑘=0
(3)
where, 𝑦𝐴𝐶[𝑛] is the analogue circuit model output, K is the
order of nonlinearity, and Q is the depth of memory length.
The multipath SI channel between the transmitter and the
receiver can be modelled with a Finite Impulse Response (FIR)
filter as [89], [98-100]:
𝑦𝑆𝐼[𝑛] = ∑ ℎ𝑚𝑥[𝑛 − 𝑚]
𝑀−1
𝑚=0
(4)
where, 𝑦𝑆𝐼[𝑛] is the SI signal, ℎ𝑚 is the m-th filter coefficient
of the equivalent digital FIR representing the channel.
The received signal after the active analogue circuit can be
written as:
𝑦𝑅𝐴[𝑛] = 𝑦𝑆𝐼[𝑛] − 𝑦𝐴𝐶 [𝑛]
= ∑ ℎ𝑚𝑥[𝑛 − 𝑚]
𝑀−1
𝑚=0
− ∑ ∑ 𝑤𝑘𝑞𝑥[𝑛 − 𝑞]|𝑥[𝑛 − 𝑘]|𝑘
𝑄−1
𝑞=0
𝐾−1
𝑘=0
= ∑ ∑ ѿ𝑘𝑞𝑥[𝑛 − 𝑝]|𝑥[𝑛 − 𝑝]|𝑘
𝑃−1
𝑝=0
𝐾−1
𝑘=0
(5)
where, P = max {M, Q} is the memory depth of the model after
active AC cancellation, ѿ0𝑝 = ℎ𝑝 − 𝑤0𝑝 and ѿ𝑘𝑝 = − 𝑤𝑘𝑝
for k > 1 are the model parameters.
Therefore the overall SI signal model comprising the AC
nonlinearity and the multipath SI channel can also be expressed
as a MP model.
To mitigate the effects of nonlinearities to SI cancellation,
[97] proposes a solution which involves two active SI
cancellation stages after passive suppression. An RF
cancellation is first performed at the input of the receiver chain
by subtracting the transmitted signal from the received signals
followed by an additional SI cancellation in the baseband
(digital domain) which estimates the SI signal channel and
regenerates it based on the estimate. The idea behind this is
increasing the precision of the regenerated SI signal, thus
increasing the amount of achievable digital SI cancellation
when operating with practical nonlinear RF components.
V. SELF-INTERFERENCE MANAGEMENT ISSUES FOR MULTI-
ANTENNA SYSTEMS
A. Other Forms of Interference for Full Duplex MIMO
Operation
In addition to the devastating SI, FD introduces other forms
of interference within the network. Whereas FD concept is able
to improve spectrum efficiency, in a multi-cell scenario, for
example, HetNets, multi-access and multi-user interferences
are introduced [3]. Apart from SI and the co-channel
interference prevalent in HD systems, there are the two major
sources of interference introduced to a multi-antenna HetNet
due to FD operation namely; UE-UE interference and BS-BS
interference.
1) UE-UE interference: this type of interference is prevalent
in smaller cells than large cells and depends on the UE locations
and their transmission powers. When the UEs have FD
capability and share same radio resources, the uplink UEs will
interfere with the downlink UEs. To mitigate this type of
interference, intelligent scheduling and coordination
mechanism are required. The goal of the coordination is to
select those UEs for simultaneous transmission such that their
rate as well as power allocation would create less interference
for each other and extract the capacity gain potential of FD
operation [11].
2) BS-BS interference: next generation networks will be
driven on the strength of dense networks. As more and more
cells are introduced, the more inter-cell interference challenge
is introduced. This becomes even more complex with FD
capability. For instance, due to simultaneous transmission and
reception at the BS, adjacent BS downlink signals would
always interfere with the UL signals in the home BS, resulting
to BS-BS interference. Techniques to mitigate BS-BS
interference are necessary to realize FD BS deployment. [11]
With FD enabled at the BS of a cellular network for instance,
BS-BS interference becomes an extremely serious issue capable
of overwhelming weak UL signals and resulting in serious loss
of UL capacity. This makes it imperative that effective SI
management scheme should seek to mitigate this source of
additional interference.
B. Modelling Perspective of Self-Interference for Multi-
antenna Systems
Modelling the SI signal is perhaps the most crucial task of an
in-band FD transceiver. Generating an accurate cancellation
signal is required both in the RF and digital domains, or else the
level of the residual SI will be too high for efficient
communication. The general structure of the considered FD
transceiver is shown in Fig. 6, where the basic operating
principles of the different SI cancellation stages are also shown.
Considering an FD system model as shown in Fig. 9, each
node is equipped with 𝑁𝑇 transmit antennas and 𝑁𝑅 receive
antennas, respectively. If 𝐇s is the 𝑁𝑅 × 𝑁𝑇 matrix of channel
gains from the 𝑁𝑇 antennas of the 𝑗-th node to the 𝑁𝑅 antennas
of the 𝑖-th node(𝑖 ≠ 𝑗), then the SI matrix of the channel gains
for the 𝑖-th node, 𝐇I can be given as: 𝑁𝑅 × 𝑁𝑇. If we consider
a SISO system, the received signal at node 1 will be given as:
𝒚(𝑡) = 𝐇s𝐱′(𝑡) + 𝐇I𝐱(𝑡) + 𝒛(𝑡) (6)
Where, the first term represents the signal of interest, the second
term represents the interfering signal and 𝒛(𝑡) is the additive
white Gaussian noise at the receiver. After quantisation, the
received signal can be rewritten as:
𝒚(𝑛) = 𝐇s𝐱′(𝑛) + 𝐇I𝐱(𝑛) + 𝒛(𝑛) (7)
If the SI is completely eliminated by using an estimated channel
defined by ∆HI in (8), then the received signal at node 1 will be:
𝒚(𝑛) = 𝐇s𝐱′(𝑛) + 𝒛(𝑛)
∆𝐇I = 𝐇I − 𝐇I′ (8)
Therefore with active cancellation, equation (6) omitting the
discrete function [n] can be rewritten as:
𝒚 = 𝐇s𝐱′ + (𝐇I − 𝐇I′)𝐱 + 𝒛 (9)
which is the received analogue signal at node 1 after
cancellation. We can therefore define the unwanted residual SI
signal at node 1 as:
y1res ≜ (𝐇I − 𝐇I′)𝐱 + 𝒛 (10)
Where, y1res is the leaked unwanted residual signal from node
1 transmitter and 𝐇I′, is an exact image of the transmit channel.
For a multi-antenna scenario, we model the received signal
according to [101] utilising pre-coding and decoding matrices
to process the SI in order to mitigate the negative effects of SI.
Let 𝐔i given as 𝑁𝑇 × 𝑑𝑖 and 𝐕i given as 𝑁𝑅 × 𝑑𝑖 be the pre-
coding and decoding matrices for the 𝑖-th node respectively,
then the signal received at the 𝑖-th node can be written as:
yi = 𝐕i†𝐇sj𝐔j + 𝐕i
†𝐇Ii𝐔i𝐱i + 𝐕i†𝒛𝑖 (11)
Where, 𝐱i (𝑑𝑖 × 1) is the vector of transmitted signals for the
𝑖-th node and 𝒛𝑖 (𝑑𝑖 × 1) is the noise at the 𝑖-th node. Whereas
the first term represents the desired signals at the 𝑖-th node, the
second term represents the SI signals suffered by operating in
FD mode. For the 𝑖-th node receiver, If we let the covariance
matrices of the direct channel representing the desired signals
be given by 𝐖𝑖,𝑗 and the SI signals be given as 𝐖i,i , then before
adding the decoding matrix we can write:
𝐖i,j = 𝐇sj𝐔j𝐏j𝐔j†𝐇sj
†
𝐖i,i = 𝐇Ii𝐔i𝐏i𝐔i†𝐇Ii
† (12)
Where, 𝑷𝑖 = {𝒙𝑖𝒙𝑖†}. The achievable rate 𝑖-th node assuming
unitary decoding matrices will therefore be written as:
𝑹𝑖 = 𝑙𝑜𝑔2 ⟦𝐈NR + (𝚪i + 𝐖i,i)−1
𝐖i,j⟧ (13)
where, 𝚪i = {𝒛𝑖𝒛𝑖†}
DAC
ADC RxRF
TxRF
Node 1 Node 2
y [n]
x[n]
hI
hs
Y(t)
x(t)ejwt x (t)ejwt
y(t)ejwt
NT
NR
NT
NR
Fig. 9. Full Duplex System Model
To be able to solve the above equations and use the
achievable rate equations, it is important to correctly model the
SI channel. In the SI Pricing approach for SI management,
[101] made use of pre-coding and decoding matrices to process
the SI in order to minimise its effects. To achieve this, the
authors estimated the SI channels while assuming that the
forward channels are perfectly known. Again, in presenting
advanced SI cancellation in a MIMO system, [97] considered a
MIMO FD transceiver model through two active SI
cancellation stages in addition to passive schemes. At the input
of the receive chain, an RF cancellation, where the transmitted
signals is subtracted from the receive signal is performed. A
further SI cancellation is performed in the digital domain
beyond the actual receive chain. This is achieved by estimating
the SI signal and then regenerating it based on the channel
estimate. However, for the scope of this survey, we do not
intend doing an in-depth modelling of SI channels for an FD-
MIMO system.
Φ
Magnitude Error(IQ Error Magnitude)
Q
I
Error Vector
Measured Signal
Ideal (Reference Signal)
Phase Error(IQ Error Phase)
Fig. 10. Error Vector Magnitude
C. Amount of Self-Interference Cancellation Required for
Full Duplex Operation
The fundamental challenge of full duplexing centers on the
isolation of the transmit path from the receive path to a level
which ensures that the transmit signal acting as a source of SI
does not affect the receivers’ sensitivity. Such huge level of
required transmit-receive isolation (in the order of 100 dB) [7]
is much larger than the isolation level needed even for
decoupling of MIMO antenna system. As already mentioned,
SI is made up of the linear components, the nonlinear
components and the transmitter noise. In [11], the authors
describe the SI – the leakage of transmitted signal in to the
receive chain - as echo. Without the cancellation of this echo
and other echoes that might result as a result of impedance
mismatch at the antenna and the reflections from the
surrounding obstacles, the desired signals may not be properly
decoded at the receiver. A big component of that echo is EVM
[68]. EVM (the difference between the error vector and the
measured symbol as depicted in Fig. 10 is the Root Mean
Square (RMS) magnitude of the error vector between the
received constellation points and the corresponding ideal
constellation points. Simply put, it is the magnitude of phase
difference as a function of time between an ideal reference
signal and the measured transmitted signal. EVM represents a
single metric / number used to describe the degradation of the
transmitted signal due to several transmitter impairments (such
as phase noise, I/Q imbalance, amplitude distortion, phase
distortion and thermal noise) already discussed [68]. EVM has
a great impact on the required amount of SI cancellation and
subsequently on FD as EVM at the local transmitter may raise
the noise floor at the local receiver thereby lowering the
sensitivity of the receiver.
To effectively isolate, the transmit signal from leaking into
the receive path, it is important to provide interference
cancellation beyond the noise floor. Noise is an ever present
factor in wireless communication systems. It deteriorates the
receive signal quality and degrades throughput in digital
systems [27]. It is known that for a communication system to
achieve FD, the radio has to cancel the self-destruct signal that
leaks from within its’ transmit chain to the receive chain. In
other words, for a receiver to detect an RF signal, the power of
the receive signal has to be up to the receiver’s sensitivity and
equal to or more than the noise floor for a good system
performance. Noise cancellation has proven to be a herculean
task. For instance [102] attempted cancelling noise components
in the digital domain, but could not arrive at a good performance
level. This is because noise cancellation in the digital domain
do not have high enough resolution in the approach employed.
The noise floor of a wireless system is the summation of all
unwanted signals, including noise and the signals generated
within the system. Though the receivers’ sensitivity is
independent of the transmitter, it is directly proportional to the
noise floor.
Given a reference bandwidth B (Hz) and Temperature To (K),
the noise power can be calculated as
𝑁 = 𝐹(𝐾𝑇𝑜)𝐵
Then the output noise is given as
𝑁𝑜𝑢𝑡 = 𝐹𝐾𝑇𝐵 (14)
Assuming a perfect amplifier, i.e., 𝐹 = 1 , and using the
above, the output noise power received at the receiver at the
room (noise) temperature (290K) is given as -174 dBm/Hz. The
in-band receiver noise floor is then calculated as:
𝑃𝑛 = −174 (𝑑𝐵𝑚
𝐻𝑧) + 𝑁𝐹 (𝑑𝐵) + 10log [𝐵 (𝐻𝑧)] (15)
where, F is the noise factor defined as the ratio of input SNR to
the output SNR, NF is the noise figure, K is the Boltzmann
constant (given as 1.38 × 10−23𝐽/𝐾 ), and W is the system
bandwidth.
For instance as shown in [7], using the calculation above,
assuming a system bandwidth of 80 MHz, a transmit power of
20 dBm and a noise figure of 5 dB, we realise that the noise
floor is -90 dBm implying that to enable FD operation in a WiFi
system a SI cancellation of 20 𝑑𝐵𝑚 – (−90 𝑑𝐵𝑚) = 110 𝑑𝐵 is required. Similarly, without loss of generality and
ignoring receiver noise figure, we present the SI cancellation
required to enable FD operations in several technologies and
generations of wireless communications systems in Table VI.
D. MIMO-Based Self-Interference Management Schemes
In an FD MIMO systems, there are several transmit and
receive antennas operating simultaneously in a transceiver. In
this scenario, the total loopback signal or SI signal coupling to
a singular receiver is a summation of all the transmitted signals
from the various transmit antennas. Unlike the SISO systems,
there are a number of other issues associated with MIMO
systems which impacts the capability for SI cancellation
techniques in a MIMO setup. These include: antenna coupling,
frequency offset, synchronisation error, etc. [25] explores three
approaches for improving the SI mitigation capabilities in FD
MIMO systems. These includes natural isolation, time-domain
cancellation and spatial suppression.
1) Precoding-Based Isolation: Precoding-based isolation
techniques for the FD MIMO systems are implemented by
employing signal processing techniques in providing additional
isolation of the transmit and the receive chain. This is different
from the physical separation discussed under passive
suppression. For instance in [33] the authors presents
precoding-based antenna isolation techniques using separated
transmit and receive antenna arrays for FD MIMO aided relays.
This technique was extended further in [103-104] where the
antennas of an FD aided relay are partitioned to let some of the
relay antennas transmit while the others receive. This
essentially introduces obstacles in the line-of-sight by either
adding a shielding plate or exploiting surrounding structures
and buildings [18]. Achieving isolation via precoding has also
been achieved by exploiting antenna directionality and making
antenna elements orthogonal [18], [33], [103]. A more practical
application of precoding in achieving natural isolation has been
implemented by using same antenna array with a duplexer for
STR. To ensure isolation is achieved, the duplexer is connected
to the antenna array which splits the input and the output feeds
[105].
2) Time-Domain Mitigation: This technique is implemented
on the assumption that the FD device is able to estimate its
transmitted signal and the loopback signal fairly accurately
[18], [25]. To do this it has to have full knowledge of the
transmitted signal to be able to replicate a mirror image signal
that can be used to cancel out the SI signal. For analog
cancellation, Time Domain (TD) cancellation algorithms such
as training-based methods can be used in both SISO and MIMO
scenarios. The TD methods can be beneficially utilized for
loopback signal leakage estimation as well as reliable SI
cancellation. In [106] the authors present a relay recorder with
prior knowledge of the interference sequence employing time-
orthogonal training in enabling a structured SI cancellation in
time domain. The structured SI cancellation technique enables
the FD device to estimate the TX-RX signal leakage path using
the knowledge of its own transmit signal. Again, for replicating
the loopback signal, [107] used time-orthogonal-training-based
algorithms. However both training methods are susceptible to
system noise and channel estimation errors which in effect
degrade the SI performance, especially in FD-MIMO systems
[25].
3) Spatial-Domain Mitigation: This technique makes use of
the spatial dimensions for receive and transmit filtering thus
offering an extra degree of freedom. This is a particularly useful
technique for multi-antenna systems where spatial domain
could offer multiple antenna systems a whole new range of SI
cancellation solutions [108]. SI mitigation in the spatial domain
is implemented using several schemes, which for this survey we
do not intend to present in great details. Some of these as
already discussed in [25], [58] and [103] include:
Antenna Subset Selection – which involves a joint
transmit and receive filter design found by calculating
the Frobenius norm for all transmit and receive
antenna array combinations and selecting the set with
least residual SI strength.
Null-space projection – in the null space projection the
precoding and decoding matrices from the Singular
Value Decomposition (SVD) of the SI channel is
selected such that the FD MIMO device is able to
direct the receive and transmit in different orthogonal
subspaces.
Joint Eigen beam Projection – joint transmit and
receive Eigen beam selection is based on the SVD of
the loopback signal. This can be achieved by
minimising the power of the SI signal by pointing the
transmit and receive beams to the minimum Eigen
modes of the loopback (SI) channel. This is called
optimal Eigen beamforming [25]. Intuitively, the
optimal joint beam selection can be solved by testing
all the TX-RX array combinations just as with antenna
subset selection. This FD assisted multi antenna SI
suppression scheme is capable of extending the
coverage area and increasing the rate [109].
VI. CHALLENGES OF SELF-INTERFERENCE MANAGEMENT IN
MULTI-CELL WIRELESS COMMUNICATION SYSTEM
The SI caused by the coupling of the transceivers’ own
transmit signal to the receiver while trying to receive signal sent
by another equipment in a cellular network has been a key
challenge that has made cellular systems largely avoid the use
of FD in the past. As is hugely evident in literature, FD systems
hold impressive promise for the next generation of cellular
networks. The scope and potential applications of FD in cellular
systems include: FD relays [44], [109], FD connection in small
cells [17], re-use of radio resources with FD transmission,
device–to–device (D2D) connection with FD, connection for
cellular backhaul with FD, and FD transmission in wireless
mesh networks. Efforts are currently made on further research
for application of FD systems on small cells for cellular
networks, ad-hoc and mesh networks and UE relay and wireless
cellular backhaul in public safety networks [6-7], [10-11], [72-
74]. This section focuses on relay networks and multi-cell
networks which are generally composed of small cells.
A. FD Relay Networks
Earliest instances of FD systems in cellular networks came
in form of relaying, where in-band repeaters as a way of
improving coverage and throughput of cellular networks,
receive, amplify and re-transmit signals on same frequency
[10], [66], [75], [77]. Radio-based FD relay (FDR) is a
promising energy and spectral efficient technology for high
speed data services [44]. While capable of improving the
service quality of users within its service range, it can also
improve the link capacity of users within its service range. Most
existing works on relays have been done on HD mode. This
owes largely to its simplicity of implementation. However,
implementing relay systems in HD mode requires extra
dedicated resources which leads to inefficiency in radio
resources use. Because of its good performance in challenging
terrains like tunnels, FD-capable relay system is a prospective
component of future FD enabled cellular networks capable of
resource conservation in contrast to HD relay systems where
additional frequency or time slot is usually dedicated for relay
transmissions. The primary challenge for FDR systems remains
how to mitigate the strong SI and residual SI, especially in small
devices scenario. Several proposals on the implementation of
same frequency FDR have been made by the Institute of
Electrical and Electronic Engineers (IEEE) and the 3GPP [111],
but the implementation still faces strong challenges including
form-factor issues, security, channel modelling and estimation,
joint radio resource management and SI management.
Traditionally, HD relay (HDR) systems are usually
employed in wireless communications. This does not help the
issue of spectrum scarcity. In order to improve the offering from
relay systems including achieving spectral efficiency
benchmarked against the traditional HDR, several relaying
schemes have been studied and reported in [66], [77], [111].
Because the focus of this paper is not on relay systems, we shall
only mention these schemes. They include: successive relaying,
two-way relaying, buffer-aided relaying, frame-level virtual FD
relaying, out-band FD relaying and IBFD relaying.
B. Challenges of SIC in a Full Duplex Multi-cell Network
Enabling FD in wireless networks will technically be more
feasible in HetNets comprising different cell sizes (pico cells,
femto cells, metro cells, micro cells and macro cells) and
different nodes (relays, remote radio heads, eNodeBs and
mobile devices) with different capabilities. Enabling FD in
cellular networks can be done under the following different
single-tier (we purposely left multi-tier network for simplicity
of explanation) network deployment scenarios depicted in Fig.
11. The BS as represented in the diagrams are FD enabled as
such have same frequency in both directions.
TABLE VI AMOUNT OF SELF-INTERFERENCE THAT NEEDS TO BE CANCELLED TO ENABLE FULL DUPLEX OPERATION
Generations Technologies / Access Technology Channel
Bandwidth
Transmit
power
Noise
power
Required SI
cancellation
1G Advanced Mobile Phone Service (AMPS) (Frequency Division Multiple Access (FDMA))
30 KHz Up to 60 dBm -129 dBm 189 dB
2G Global Systems for Mobile Communications (GSM) (Time Division
Multiple Access (TDMA))
200 KHz 36 dBm -121 dBm 157 dB
Code Division Multiple Access (CDMA) 1.25 MHz 48 dBm -113 dBm 161 dB
2.5G General Packet Radio Service (GPRS) 200 KHz 39 dBm -121 dBm 160 dB
Enhanced Data Rate for GSM Evolution (EDGE) 200 KHz -121 dBm
3G Wideband Code Division Multiple Access (WCDMA) / Universal
Mobile Telecommunications Systems (UMTS)
5 MHz 43 dBm -107 dBm 150 dB
Code Division Multiple Access (CDMA) 2000
1.25 MHz -113 dBm 156 dB
3.5G High speed Uplink / Downlink Packet Access (HSUPA / HSDPA) 5 MHz 43 dBm -107 dBm 150 dB
Evolution-Data Optimised (EVDO) 1.25 MHz -113 dBm 156 dB
3.75G Long Term Evolution (LTE) (Orthogonal / Single Carrier Frequency
Division Multiple Access) (OFDMA / SC-FDMA)
20 MHz 46 dBm -101 dBm 147 dB
Worldwide Interoperability for Microwave Access
(WIMAX) (Scalable Orthogonal Frequency Division
Multiple Access (SOFDMA)
Fixed
WiMAX
10 MHz -104 dBm 150 dB
4G Long Term Evolution Advanced (LTE-A) (Orthogonal / Single Carrier
Frequency Division Multiple Access) (OFDMA / SC-FDMA)
20 MHz 46 dBm
-101 dBm 147 dB
Worldwide Interoperability for Microwave Access
(WIMAX) (Scalable Orthogonal Frequency Division
Multiple Access (SOFDMA)
Mobile
WiMAX
10 MHz 43 dBm -104 dBm 150 dB
5G Beam Division Multiple Access (BDMA) and the non-and-quasi-
orthogonal or Filter Bank Multi-Carrier (FBMC) multiple access
60 GHz 20 dBm -96 dBm 116 dB
802.11ac - Gigabit Wi-Fi (taunted as 5G Wi-Fi) 20, 40, 80, 160 MHz
-91 dBm 112 dB
802.11ad - Wireless Gigabit (Microwave Wi-Fi) 2 GHz -81 dBm 101 dB
802.11af – White-Fi 5, 10, 20, 40
MHz
-98 dBm 118 dB
1) FD enabled BS and HD enabled UE: This scenario
involves deploying an FD capable BS and HD enabled MTs. In
this scenario as shown in Fig. 11(a), the FD BS is able to
simultaneously communicate with both the DL and the UL
users with the former receiving data from the BS and the later
transmitting data to the BS. The challenges of this scenario
include: accurately measuring co-channel interference at
different nodes and backhauling it to the BS, user-scheduling
for maximum gain and effectively managing additional
interference caused by leakages from signals transmitted from
neighbouring bands.
2) FD enabled BS and FD enabled UE: In this scenario
shown in Fig. 11(b), both the BS and the UE are FD enabled
with the BS expected to constantly establish FD links to
scheduled UEs. This scenario holds prospects for a no co-
channel interference situation and holds potential for doubling
the overall spectral efficiency compared to the traditional HD
only systems, since the BS is simultaneously transmitting and
receiving. This could ultimately be the future implementation
of FD wireless communication. However SI on the BS and also
on the UE threatens to dominate performance. Just like in the
scenario described in Fig. 11(a), this scenario induces inter-user
interference which is not obtainable with HD only systems. In
this case the UL transmission causes interference to the DL
reception and in cases of strong inter-user interference in the
system even with SI mitigated, this problem can easily erode
the gains of FD if not properly mitigated.
3) FD enabled BS with both FD and HD enabled UEs
coexisting: This scenario has been identified as a futuristic
prospect for FD cellular systems where an FD BS serves both
FD and HD UEs all coexisting in same cell. As shown in Fig.
11(c), an FD BS serves a mix of users some of which are
capable of simultaneous transmit and receive on same radio
resources and others operating as traditional HD terminals
which could only transmit on an UL frequency and receive on
a different DL frequency or use different time slots to
accomplish their UL and DL transmissions.
4) FD D2D communication: A D2D communication involves
the source and destination devices exchanging data with each
other without the involvement of the base station, though could
be supported by the base station for link information [112]. The
idea behind this scenario is the ability of two users to
communicate as FD devices on the unused macro resource if
none of their neighbours are using it. As illustrated
in Fig. 12, if UE2 and UE3 are FD enabled, they could reuse
UE1’s UL resources for their FD communication. Also, the
D2D performance between them could be improved. A
different Radio Resource Allocation (RRA) method could see
UE2 reusing its macro DL resources for its transmission to UE3
whereas UE3 uses UE1’s macro uplink channel for its
transmission to UE2 [7].
(c) FD BS with both FD UE and HD UEs
UE
UE UE
UE
UEUE
UE
BS BS BS
(a) FD BS with HD UEs (b) FD BS with FD UEs
Fig.11. Full duplex network deployment scenarios
UE1UE3
UE2
BS
Fig. 12. A small cell with FD BS and FD D2D UEs
VII. LESSONS LEARNT, OPEN RESEARCH ISSUES AND FUTURE
DIRECTION
In this section we present the lessons learnt from surveying
the different SI management schemes. These then form the
basis for the directions and likely challenges for future work for
implementing appropriate SI mitigation scheme with less
complexities and costs but capable of enabling single frequency
FD systems in the mobile wireless networks.
A. Lessons Learnt
1) The evolution of future networks tends towards small cells,
ad-hoc and mesh networks in a dense environment. A network
comprising of small cells having the capability for connecting
users to the base station and supporting STR could greatly
improve the SE of the system. However, this throws up several
other sources of interference apart from SI. One of the things
that we have learnt is that there is not a single fully operational
system that has incorporated the increased effects of co-
channel, multi-cell, and SI due to FD operation.
2) No single method is all sufficient. Work done so far
suggests that a hybrid combination of passive suppression and
active cancellation could prove more effective for SI
management in FD systems. Literature also has it that the
effectiveness of digital cancellation has a reliance on analogue
cancellation. For instance, with Double Talk technique in
SatCom, a cancellation of 30-35 dBm achieved with DSP in the
‘back-end’ is not enough to enable FD operation. In essence,
this must be augmented by careful and ingenious front-end
analogue design to provide the requisite extra interference
cancellation. Again, none of the solutions seem to have
adequately nailed that trade off or equilibrium level that defines
a cost effective balance of passive suppression and active
cancellation for effective SI management. For efficient Digital
SI cancellation, there should be an effective analogue design
and cancellation.
3) Sufficient Passive Suppression methods only may be
difficult to attain. Especially with small form factor devices, it
is difficult achieving a reasonable TX-RX isolation capable of
enabling FD functionality.
4) Frequency dependent solutions may not aid digital
cancellation schemes especially in MIMO systems. The fact
that most RF components are frequency sensitive, designing a
one-fits-all solution for frequency varying circuits is a
challenge, especially in wideband scenarios.
5) SI cancellation in MIMO systems suffers the inherent
problems with MIMO multi antenna issues. MIMO already has
the challenges posed by antenna coupling, synchronization
error and frequency offset. These challenges becomes even
more pronounced in FD-MIMO system making it more difficult
for SI management.
6) Transmitter noise induced distortion in an FD receiver is an
important factor that impacts the SI cancellation capability of
the system. For any efficient cancellation scheme, it is
important to model the linear, nonlinear components and
understand EVM impact for more efficient antenna design. A
good example is the SI mitigation technique in SatCom
requiring an on-board relay system. The noise generated by the
receiver systems on both the uplink and downlink directions is
as equally important as the SI channel.
7) Though there is minimal isolation problem with satellite
systems compared to the very formidable isolation issue with
the terrestrial systems, the power imbalance in SatCom system
is enormous. Whereas studies have demonstrated the possibility
of mitigating the SI using the on-board relay system in
satellites, to realise FD satellite system, further comprehensive
investigation need to be carried out to study the practicality of
estimating the on-board SI channel to a high accuracy.
B. Self-Interference Management in Small form-factor
Devices
Most of the state-of-the-art SI mitigation schemes focus on
small form-factor devices e.g., as shown in Table VI. For
instance, DUPLO project, like many others target small cell
scenarios implying that FD transceiver designs must consider
small form-factor devices which support integration into
commercially viable compact radio devices. Whereas the idea
behind this can hardly be faulted, first because most devices
operating on wireless communication networks (e.g., smart
phones, some relay systems, etc.) are of small sizes and second
and importantly because future networks are targeting small
cells. Antenna miniaturization (antenna size reduction) suffers
extremely limited bandwidth and other technical difficulties
due to space and shape constraints. As already noted, relay
systems are mostly of a small form-factor size and to be able to
enable FD in such systems further research needs to be done.
Furthermore, most schemes reported in literature are
implemented within the propagation and active analogue-
domain spheres. This makes it a challenge having the required
space for isolation that could enable FD as well as enough space
for the analogue circuits. A possible future direction in
addressing miniaturization of the antenna systems could be
investigating the possibility of a planar antenna device capable
of doing FD both on small form-factor devices but which could
also be integrated on larger microwave and, or millimeter-wave
solutions.
C. Improving the Hardware circuitry and channel modelling
In theory, FD can potentially double the spectral efficiency of
a communication system. This is however feasible if the system
has infinite dynamic range, perfect channel estimation and is
able to perfectly suppress SI signal. This prospect is threatened
by hardware limitations including transceiver signal
quantisation, I/Q imbalance and nonlinearities. As already
pointed out in the work of [7], the analogue 10x10 cm PCB
design is capable of cancelling the SI generated in a WiFi
network up to the noise floor including the linear, nonlinear and
transmitter noise components. However, it only supports SISO
scenarios. To introduce this solution to the multi-antenna
systems, an analogue chip that is able to cancel distortions
across multiple antennas and capable of dynamic adaptation in
terms of changing environmental conditions needs to be
developed. Carrying out SI mitigation increases both the
complexities and cost of FD devices. A SI cancellation solution
for MIMO will often require extra analogue circuitry with more
power consuming components. It is therefore noteworthy to
balance power consumption, and cost against SI management
performance when designing the SI cancellation circuitry.
Finding that point of balanced tradeoff between SI cancellation
and the associated cost of hardware required to accurately
deliver improved SI cancellation is a huge challenge. Moreover
the strength of digital-domain cancellation relies on the
systems’ knowledge of the CSI both on the transmitter and the
receiver side. Not many studies have done an actual
characterization of the SI channel, as most have assumed SI
follows Gaussian distribution, Rayleigh distribution or
Nakagami–m distribution [3]. It will be interesting besides
designing a more efficient circuitry, modelling accurate and
effective channels that capture the residual SI, and the
distortions introduced in the channel by the transceiver in the
mold of the hybrid analogue-digital design proposed in [7]
which accurately model all the linear, nonlinear distortions as
well as the transmitter noise.
D. Implementing Self-Interference Cancellation in FD MIMO
Scenarios
The simple fact that the capacity of a MIMO system grows
linearly with the minimum of the number of transmit or receive
antenna without needing extra radio resources shows that the
performance of a cellular network can be improved by
employing multiple antennas. However, antenna systems in
MIMO systems suffer from coupling and synchronization error
as well as cross talk when the antenna modules are placed so
close to each other. For instance, the work of [7] seems to be
holistic in mitigating SI up to the noise floor, albeit for SISO
systems. Whereas the general design of the system is not
frequency dependent, most of the components used within the
analogue circuitry are frequency dependent and can only work
well in a given frequency range. The design targeted only SISO
scenarios which effectively makes it impractical to enable FD
in MIMO scenarios using the technique described. Extending
their work to MIMO scenarios, never mind massive MIMO will
require a novel design to handle cross talks and other distortions
coming from closely arranged MIMO antenna modules and
hardware impairments. It is obvious that active cancellation
mechanism relies substantially on the precision of cancellation
signal. Therefore the hardware impairments such as phase noise
and I/Q imbalance limit the cancellation performance. It is
therefore imperative that as a future direction the design of a
comprehensive model incorporating the different hardware
impairments capable of coping with the transmitter noise
difficult to compensate for in the baseband [46] is pursued. This
in turn will require an accurate mathematical modelling and
statistical characterisation of the SI channel to serve as the basis
for performance-matrix analysis, e.g., achievable rate, as well
as system design. In view of this, a future direction would be
investigating the impact of antenna correlation, antenna non-
linearity, effects of synchronization error, gain /phase offset,
carrier offset, In-phase Quadrature (I/Q) imbalance and other
non-idealities within the baseband receiver in enabling FD in
MIMO systems.
E. Radio Resource Management and Multi-user diversity
schemes
FD technology definitely offers extra degree of freedom by
allowing the whole spectrum to be used in both forward and
reverse transmission direction. This will sure increase the
available multiuser diversity in the communication systems. It
is important to note that SI may not be fully mitigated when
performing resource allocation. Therefore, to fully harvest the
increased multiuser diversity and address the SI problem, new
multiple access techniques needs to be proposed and evaluated.
These should be such capable of supporting different duplexing
scenarios such as point-to-point FD and point-to-multipoint FD
scenarios. Again, designing radio resource management
algorithms that take into account the features of FD are essential
to improving the multiple access techniques.
Resource management plays important roles in energy
efficiency spectrum efficiency and quality of service
provisioning [113]. Since SI is involved in FDR networks, how
to dynamically allocate the space-time-frequency resources
becomes even more important and challenging than in the
traditional wireless networks. Take beamforming for example,
while one can electronically steer transmit and receive weight
of different antenna elements for the purpose of SI mitigation,
this may also unintentionally reduce the power radiated on the
desired signals and hence degrade the system performance /
service quality. Similar phenomenon exists in other resource
allocation processes, such as power allocation, antenna
selection and relay node selection. Therefore effective resource
management approaches should balance the performance of SI
mitigation and other systems measures.
Though it is easy to infer from literature that so much work
is being done in designing and implementing FD capable
devices and systems, the impact of FD on the capacity and the
energy of heterogeneous networks have not be sufficiently
analysed. Whereas some work have been done regarding
resource management and SI management such as [114] which
evaluated FD operations in a small cell cellular scenario by
implementing a joint UL and DL beamforming for a single cell
and [115], which studied a joint radio resource allocation for
the UL and DL in an FD system, there still exists some gaps.
For example, while the work of [115] considered a non-
cooperative power allocation algorithm, the DUPLO
experiment did not consider the multi-user diversity gain that
could be derived by appropriate power adjustments and user
scheduling. Not only is this approach suboptimal, the paper did
not take into account the inter-user interference generated in the
system when a user is allowed to increase its power arbitrarily
and its impact on the SI cancellation capability of the system.
An effective resource management approach which is capable
of SI mitigation while deriving the gains of FD without
degrading the system performance remains a challenge yet to
be resolved. These possibilities could steer a future direction.
F. Implementing Cost Efficient Spatial Domain Solutions
Separating transmit and receive antennas in space presents a
simple SI passive suppression scheme, especially for SISO
systems. However, implementing spatial-domain suppression
schemes for MIMO systems come with an extra complexity
burden capable of limiting the SI mitigation ability of the
system. This results from the very complex matrix
computations required in this scenario. As a future direction for
achieving FD in wireless networks, it is imperative that more
cost-efficient spatial domain SI suppression algorithms for
MIMO channels have to be designed.
G. Interference management in heterogeneous networks
Future networks with dense heterogeneous cells presents
multiple sources of interference in addition to SI which in turn
presents a challenge to SI management and makes the
implementation of FD more complicated. This situation is made
worse in a heterogeneous multi-tier, multi-cell hierarchical
structure where as the number of small cells increase, so also
are there multiple sources of inter-cell interference, BS-BS
interference as well as UE-UE interference in cases where the
user terminal is also FD enabled with a reuse factor of one. In
this case, radio resource management increasingly becomes
complex and challenging. Whereas the prospect for increased
SE exists, this will only be obtained using an efficient radio
resource management scheme and effective power allocation
scheme. Finding this practical balance between system
performances against the performance of SI management
schemes is an interesting future direction.
VIII. CONCLUSION
This article discusses the state-of-the-art on SI mitigation
schemes for enabling single frequency FD networks. The
benefits of FD systems over HD systems are huge. FD systems
are capable of potentially doubling the spectral efficiency of the
network. However, the major challenge hampering the
implementation of this technology in practical mobile
communication systems is the very destructive effects of signal
leakages from the transmit chain to the receive chain. This
leakage can be several millions (>100 dB) more than the
received signal thereby suppressing the desired useful signals.
To harness the gains of FD, the SI has to be mitigated and
suppressed to or nearly the receiver noise floor. This paper has
highlighted the schemes for SI mitigation available in literature
by classifying and comparing their pros and cons. The
mitigation schemes are either active or passive and the
processes take place in any of the following domains: the
propagation domain, analogue circuit domain or digital circuit
domain. These are also impacted by some transceiver non-
idealities. Furthermore, we classified the available schemes for
SI mitigation and showed through figures and tables the
capabilities, advantages and disadvantages. Whereas a
combination of some schemes has shown proven results for
cancelling SI up to the noise floor, the challenges facing the
implementation of such schemes are also highlighted leading us
to identifying some open research issues and proposing future
research directions towards realising FD cellular networks.
These include: improving the small form-factor solutions, the
hybrid analogue-digital solutions, improvement of the analogue
circuitry, implementing effective SI mitigation techniques for
FD in MIMO scenarios and design of efficient RRM techniques
that could aid SI mitigation. Our intention with this survey is to
present the mitigation schemes available in literature while
highlighting the pending practical challenges for adequately
mitigating SI and enabling FD operation in wireless systems.
While it is fair to say that there are many SI mitigating schemes
already studied and implemented with varying degrees of
success, it is also important to note that a lot still needs to be
done in order to design a less complex, easily implementable
scheme that provides sufficient mitigating capability for
enabling FD operations in wireless communication systems.
ACKNOWLEDGEMENT
The views expressed here are those of the authors and do not
necessarily reflect those of the affiliated organisations. The
authors would like to acknowledge the support of the University
of Surrey 5GIC (http://www.surrey.ac.uk/5gic) members for
this work.
REFERENCES
[1] E. Pateromichelakis, “Inter-cell Interference-aware Radio Resource Management for Femtocell Networks,” A PhD Thesis submitted to the
University of Surrey, July 2013.
[2] C. Kosta, “Inter-cell Interference Coordination in multi-cellular networks,” A PhD Thesis submitted to the University of Surrey,
September, 2013.
[3] G. Liu, F. R. Yu, H. Ji, V. C. M. Leung and X. Li, “In-Band Full-Duplex Relaying: A Survey, Research Issues and Challenges,” IEEE
communications Surveys & Tutorial, vol.17, No. 2, second quarter, 2015.
[4] Future works: White paper – Looking ahead to 5G. [Online]. Available: http//:www.networks.nokia.com/file/28771/5g-white-paper
[5] D. Kim, H. Lee and D. Hong, "A Survey of In-Band Full-Duplex
Transmission: From the Perspective of PHY and MAC Layers," in IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 2017-2046,
2015.
[6] J. Choi, M. Jain, K. Srinivasan, P. Levis and S. Katti, “Achieving Single Channel, Full Duplex Wireless Communication,” ACM MobiCom,
September, 2010, Chicago, Illinois, USA.
[7] D. Bharadia, E. McMilin and S. Katti, “Full Duplex Radios,” ACM SIGCOMM Computer Communication Review, vol. 43, Issue 4, pp. 375-
386, New York, October 2013.
[8] The ITU Radiocommunication Assembly, "Terms and Definitions," Rec.
B.13, ITU-R V.662-2 1, Recommendation, ITU-R V.662-2 (1986-1990-
1993). [Online]. Available: https://www.itu.int/dms_pubrec/itu-r/rec/v/R-
REC-V.662-2-199304-S!!PDF-E.pdf [9] Ericsson, Nokia and Nokia Siemens Networks, “Half Duplex FDD in
LTE”, TSG-RAN WG1 #51bis, R1-080534, Sevilla, Spain. January 14-
18, 2008. [10] A. Sabharwal, P. Schinter, D. Guo, D.W. Bliss, S. Rangarajan and R.
Wichman, “In-Band Full-Duplex Wireless: Challenges and
Opportunities,” IEEE Journal on selected areas in comms., vol. 32, No. 9, pp. 1637 – 1652, September, 2014.
[11] Y. Choi and H. Shirani-Mehr, “Simultaneous Transmission and
Reception: Algorithm, Design and System Level Performance,” IEEE transactions on Wireless Comms., vol. 12, No. 12, December, 2013.
[12] T. Riihonen, S. Werner and R. Wichman, “Comparison of Full-
Duplexand Half-Duplex Modes with a Fixed Amplify-and-Forward Relay,” IEEE Wireless Communications and Networking Conference,
2009, pp. 1-5.
[13] Z. Zhang, X. Chai, K. Long, A.V. Vasilakos and L. Hanzo, “Full Duplex Techniques for 5G Networks: Self-interference Cancellation, Protocol
Design, and Relay Selection, IEEE Communications Magazine, May
2015. [14] A. K. Khandani, “Two-way (true full-duplex) wireless,” Proc. 13th Can.
Workshop Inform. Theory, pp. 33-38, Toronto, ON, Canada.
[15] M. Duarte and A. Sabharwal, “Full-duplex wireless communications using off-the-shelf radios: Feasibility and first results,” Proc. Asilomar
Conf. Signals, Syst. Comput., pp. 1558-1562, Pacific Grove, CA, USA.
[16] M. Duarte, C. Dick and A. Sabharwal, “Experiment-driven characterization of full-duplex wireless systems,” IEEE Trans. Wireless
Comm., vol. 11, no. 12, pp. 4296-4307, Dec., 2012.
[17] P. Persson, M. Coldrey, A. Wolfgang and P. Bohlin, “Design and Evaluation of a 2 2 MIMO Repeater,” Proc. 3rd Eur. Conf. Antennas
Propag., pp. 1509-1512, Berlin, Germany.
[18] T. Riihonen, S. Werner and R. Wichman, “Mitigation of loopback self-interference in full-duplex MIMO relays,” IEEE Trans. Signal Proc., vol.
59, no. 12, pp. 5983-5993, Dec., 2011.
[19] C. Psomas, C. Skouroumounis, I. Krikidis, A. Kalis, Z. Theodosiou and A. Kounoudes, “Performance Gains from Directional Antennas in Full-
Duplex Systems,” IEEE Int. Conf. on Micr., Comms., Ant., and Elec.
Systems (COMCAS 2015) Nov, 2015. [20] B. Yin, M. Wu, C. Studer, J. R. Cavallaro, and J. Lilleberg, “Full-Duplex
in Large-Scale Wireless Systems”, Asilomar Conference on Signals,
Systems and computers, pp. 1623-1627, November 2013, Pacific Grove, CA.
[21] FP7-PEOPLE-2010-IRSES - Marie Curie Action "International Research
Staff Exchange Scheme," Community Research and Development Information Service (CORDIS). [Online]. Available:
http://cordis.europa.eu/programme/rcn/12704_en.h
[22] Full Duplex Radios for Local Access (DUPLO) Technical Report, DUPLO Deliverable D4.1, “Performance of Full-Duplex systems,”
January, 2014. [23] H. Alves, R. D. Souza and M. E. Pellenz, "Brief survey on full-duplex
relaying and its applications on 5G," Computer Aided Modelling and
Design of Communication Links and Networks (CAMAD), 2015 IEEE 20th International Workshop on, Guildford, 2015, pp. 17-21.
[24] M. Amjad, F. Akhtar, M. H. Rehmani, M. Reisslein, T. Umer, “Full-
Duplex Communication in Cognitive Radio Networks: A Survey,” in IEEE Communications Surveys & Tutorials, vol.PP, no.99, pp.1-1
[25] Z. Zhang, K. Long, A. V. Vasilakos and L. Hanzo, "Full-Duplex Wireless
Communications: Challenges, Solutions, and Future Research Directions," in Proceedings of the IEEE, vol. 104, no. 7, pp. 1369-1409,
July 2016.
[26] F. Gunnarsson, M.N Johansson, A. Furuskar, M. Lundevall, A. Simonsson, C. Tidestav, and M. Blomgren, “Downtilted Base Station
Antennas – A Simulation Model Proposal and Impact on HSPA and LTE
Performance,” IEEE 68th Vehicular Technology Conference, VTC 2008 – Fall, September, 2008.
[27] E. Everett, M. Duarte, C. Dick, and A. Sabharwal, “Empowering full
duplex wireless communication by exploiting directional diversity,” in Proc. Asilomar Conf. Signals, Syst. Comput., 2011, pp. 2002–2006.
[28] H. Hamazumi, K. Imamura, N. Iai, K. Shibuya, and M. Sasaki, “A study
of a loop interference canceller for the relay stations in an SFN for digital terrestrial broadcasting,” in Proc. IEEE Global Telecommun.Conf., 2000,
vol. 1, pp. 167–171.
[29] P. Beasley, A. Stove, B. Reits, and B. As, “Solving the problems of a single antenna frequency modulated CW radar,” in Proc. IEEE Int. Radar
Conf., 1990, pp. 391–395.
[30] M. Cryan, P. Hall, S. Tsang, and J. Sha, “Integrated active antenna with full duplex operation,” IEEE Trans. Microw. Theory Tech., vol. 45, no.
10, pp. 1742–1748, Oct. 1997.
[31] S. Chen, M. A. Beach, and J. P. McGeehan, “Division-free duplex for wireless applications,” IEEE Electron. Lett., vol. 34, no. 2, pp. 147–148,
Jan. 1998.
[32] B. Basheer and S.Mathews, “Active Self-interference Cancellation Techniques in Full Duplex Communications – A Survey,” International
Journal of Research in engineering and Technology, vol. 3, Issue 1, pp.
92-96, March 2014. [33] C. R. Anderson et al., “Antenna isolation, wideband multipath
propagation measurements, and interference mitigation for on-frequency
repeaters,” in Proc. IEEE Southeast Con, Mar. 2004, pp. 110–114.
[34] W.K. Kim et al., “A passive circulator for RFID application with high
isolation using a directional coupler,” in Proc. 36th Eur. Microw. Conf.,
pp. 196–199, 2006. [35] C.Y. Kim, J.G. Kim, and S. Hong, “A quadrature radar topology with TX
leakage canceller for 24-GHz radar applications,” IEEE Trans.
Microw.Theory Tech., vol. 55, no. 7, pp. 1438–1444, Jul. 2007. [36] J. G. Kim, S. Ko, S. Jeon, J. W. Park, and S. Hong, “Balanced topology
to cancel Tx leakage in CW radar,” IEEE Microw. Wireless Compon.
Lett., vol. 14, no. 9, pp. 443–445, Sep. 2004.
[37] P. Lioliou, M. Viberg, M. Coldrey, and F. Athley, “Self-interference suppression in full-duplex MIMO relays,” in Proc. 44th Asilomar Conf.
on Signals, Systems and Computers, 2010, pp. 658–662.
[38] D. W. Bliss, P. A. Parker, and A. R. Margetts, “Simultaneous transmission and reception for improved wireless network performance,” in Proc.
IEEE Statist. Signal Process. Workshop, Aug. 2007, pp. 478–482.
[39] M. Duarte et al., “Design and characterization of a full-duplex multiantenna system for WiFi networks,” IEEE Trans. Veh. Commun.,
vol. 63, no. 3, pp. 1160–1177, Mar. 2014.
[40] A. Sahai, G. Patel, and A. Sabharwal, Pushing the Limits of Full-Duplex: Design and Real-Time Implementation, Rice University, Houston, TX,
USA, Tech. Rep. TREE1104.
[41] B. Chun and Y. H. Lee, “A spatial self-interference nullification method for full duplex amplify-and-forward MIMO relays,” in Proc. IEEE
WCNC, Apr. 2010, pp. 1–6.
[42] D. Senaratne and C. Tellambura, “Beamforming for space division duplexing,” in Proc. IEEE ICC, Jun. 2011, pp. 1–5.
[43] T. Snow, C. Fulton, and W. J. Chappell, “Transmit–receive duplexing
using digital beamforming system to cancel self-interference,” IEEE Trans. Microw. Theory Tech., vol. 59, no. 12, pp. 3494–3503, Dec. 2011.
[44] L. Zhang, W. Liu and J. Li, "Low-Complexity Distributed Beamforming
for Relay Networks With Real-Valued Implementation," in IEEE Transactions on Signal Processing, vol. 61, no. 20, pp. 5039-5048,
Oct.15, 2013.
[45] L. Zhang, W. Liu, A. ul Quddus, M. Dianati and R. Tafazolli, "Adaptive Distributed Beamforming for Relay Networks Based on Local Channel
State Information," in IEEE Transactions on Signal and Information Processing over Networks, vol. 1, no. 2, pp. 117-128, June 2015.
[46] T.L. Marzetta, “Noncooperative cellular wireless with unlimited numbers
of base station antennas,” IEEE Trans. Wireless Commun., vol. 9, no. 11, pp. 3590-3600, Nov. 2010.
[47] E. Aryafar, M. A. Khojastepour, K. Sundaresan, S. Rangarajan, and M.
Chiang, “MIDU: enabling MIMO full duplex,” in Proc. ACM MobiCom, 2012, pp. 257–268.
[48] T. Riihonen et al., “Optimal Eigen beamforming for suppressing self-
interference in full-duplex MIMO relays,” in Proc. 45th Annu. CISS, 2011, pp. 1–6.
[49] E. Everett, “Full-duplex infrastructure nodes: Achieving long range with
half-duplex mobiles,” M.S. thesis, Rice University, Houston, TX, USA, 2012.
[50] B. Day, A. Margetts, D. Bliss, and P. Schniter, “Full-duplex MIMO
relaying: Achievable rates under limited dynamic range,” IEEE J. Sel.Areas Commun., vol. 30, no. 8, pp. 1541–1553, Sep. 2012.
[51] F. O’Hara and G. Moore, “A high performance CW receiver using feed
thru nulling,” Microw. Journal, vol. 6, pp. 63–71, Sep. 1963. [52] A. G. Stove, “Linear FMCW radar techniques,” Proc. Inst. Elect. Eng. F
(Radar Signal Process), vol. 139, no. 5, pp. 343–350, Oct. 1992.
[53] H. Suzuki, K. Itoh, Y. Ebine, and M. Sato, “A booster configuration with adaptive reduction of transmitter–receiver antenna coupling for pager
systems,” in Proc. IEEE Veh. Technol. Conf.—Fall, 1999, vol. 3, pp.
1516–1520. [54] K. Lin, Y. E. Wang, C.-K. Pao, and Y.-C. Shih, “A Ka-Band FMCW radar
front-end with adaptive leakage cancellation,” IEEE Trans. Microw.
Theory Tech., vol. 54, no. 12, pp. 4041–4048, Dec. 2006. [55] J.W. Jung et al., “TX leakage Cancellation via a micro controller and high
TX-to-RX isolations covering an UHF RFID frequency band of 908 to
914 MHz,” IEEE Microwave Wireless Comp on. Letters, vol. 18, No. 10, pp. 710–712, Oct. 2008.
[56] S. Goyal, P. Liu, S. Hua and S. Panwar, “Analyzing a Full-Duplex
Cellular System,” in 47th Conference on Information Sciences and Systems (CISS), March 2013.
[57] M. Jain, J. I. Choi, T. Kim, D. Bharadia, S. Seth, K. Srinivasan, P. Levis,S.
Katti, and P. Sinha, “Practical, real-time, full duplex wireless,” in Proc. ACM MobiCom, 2011, pp. 301–312.
[58] T. Riihonen, S. Werner, and R. Wichman, “Residual self-interference in
full-duplex MIMO relays after null-space projection and cancellation,” in Proc. Asilomar Conf. Signals, Syst. Comput., Nov. 2010, pp. 653–657.
[59] B. Day, A. Margetts, D. Bliss, and P. Schniter, “Full-duplex bidirectional
MIMO: Achievable rates under limited dynamic range,” IEEE Trans. of signal Process., vol. 60, no. 7, pp. 3702–3713, 2012.
[60] E. Everett, A. Sahai, and A. Sabharwal, “Passive self-interference
suppression for full-duplex infrastructure nodes,” IEEE Trans. Wireless Commun., vol. 13, no. 2, pp. 680–694, Feb. 2014.
[61] B. Chun, E.-R. Jeong, J. Joung, Y. Oh, and Y. H. Lee, “Pre-nulling for self-interference suppression in full-duplex relays,” in Proc. APSIPA
ASC, 2009, pp. 91–97.
[62] J. Ma, G. Li, J. Zhang, T. Kuze, and H. Iura, “A new coupling channel estimator for cross-talk cancellation at wireless relay stations,” in Proc.
IEEE Global Telecommun. Conf., 2009, pp. 1–6.
[63] E. A. Rodriguez, R. L. Valcarce, T. Riihonen, S. Werner, and R. Wichman, “Autocorrelation-based adaptation rule for feedback
equalization in wideband full-duplex amplify-and- forward MIMO
relays,” in Proc. IEEE ICASSP, pp. 4968–4972, , May 2013. [64] T. M. Cover and A. E. Gamal, “Capacity theorems for the relay channel,”
IEEE Trans. Inform. Theory, vol. 25, no. 5, pp. 572–584, Sep. 1979.
[65] M. Wu, B. Yin, A. Vosoughi, C. Studer, J.R. Cavallaro, and C. Dick, “Approximate matrix inversion for high-throughput data detection on the
large-scale MIMO uplink,” in Proc. IEEE ISCAS, Beijing, China, May
2013, pp. 2155-2158. [66] D. W. Bliss, T. M. Hancock, and P. Schniter, “Hardware
phenomenological effects on cochannel full-duplex MIMO relay
performance,” in Proc. IEEE Asilomar Conf. Signals, Syst. Comput., 2012, pp. 34–39.
[67] A. Sendonaris, E. Erkip, and B. Aazhang, “User cooperation diversity part
I: System description,” IEEE Trans. Comm., vol. 51, no. 11, pp. 1927–1938, Nov. 2003.
[68] Full Duplex Radios for Local Access (DUPLO) Technical Report,
DUPLO Deliverable D1.1, “System Scenarios and Technical Requirements for Full-Duplex Concept,” April, 2013.
[69] A. Goldsmith. Wireless Communications. Cambridge University Press, NY, USA, 2005.
[70] S. Barghi, A. Khojastepour, K. Sundaresan and S. Rangarajan,
“Characterizing the Throughput Gain of Single Cell MIMO Wireless Systems with Full Duplex Radios,” 10th Intl., Symp. on Modeling and
Optimization in Mobile, Ad Hoc Wireless Networks (WiOpt), May, 2012.
[71] J. N. Laneman, D. N. C. Tse, and G. W. Wornell, “Cooperative diversity in wireless networks: Efficient protocols and outage behaviour,” IEEE
Trans. Inform. Theory, vol. 50, no. 12, pp. 3062–3080, Dec. 2004.
[72] S. Li and R. D. Murch, "An Investigation Into Baseband Techniques for Single-Channel Full-Duplex Wireless Communication Systems," in IEEE
Transactions on Wireless Communications, vol. 13, no. 9, pp. 4794-4806,
Sept. 2014. [73] A. Sahai, G. Patel, C. Dick, and A. Sabharwal, “Understanding the impact
of phase noise on active cancellation in wireless full-duplex,” in Proc.
Asilomar Conf. Signals, Syst. Comput., 2012, pp. 29–33. [74] A. Sahai, G. Patel, C. Dick, and A. Sabharwal, “On the impact of phase
noise on active cancellation in wireless full-duplex,” IEEE Trans. Veh.
Technol., vol. 62, no. 9, pp. 4494–4510, Nov. 2013. [75] E. Ahmed, A. M Eltawil and A, Sabharwal, “Rate Gain Region and
Design Tradeoffs for Full-Duplex Wireless Communications,” IEEE
Transactions on Wireless Communications, vol. 12, No. 7, pp. 3556 – 3906, July 2013.
[76] J. Sangiamwong, T. Asai, J. Hagiwara, Y. Okumura, and T. Ohya, “Joint
multi-filter design for full-duplex MU-MIMO relaying,” in Proc. IEEE VTC—Spring, 2009, pp. 1–5.
[77] V. Syrjala, M. Valkama, L. Anttila, T. Riihonen, and D. Korpi, “Analysis
of oscillator phase-noise effects on self-interference cancellation in dull-duplex OFDM radio transceivers,” IEEE Trans. Wireless Commun., vol.
13, no. 6, pp. 2977–2990, Jun. 2014.
[78] L. Laughling, M.A. Beach, K.A. Morris and J.L. Haine, “ Optimum Single Antenna Full Duplex Using Hybrid Junctions,” IEEE Journal on sel.
Areas in comms., vol. 32, no. 9, September, 2014.
[79] B. Debaillie, D.J. van den Broek, C. Lavin, B. van Liempd, E.A.M. Klumperink, C. Palacios, J. Crannickx and B. Nauta, “RF Self-Intererence
Reduction for Compact Full-Duplex Radios,” IEEE 81st Veh. Tech. Conf
(VTC Spring), Glasgow, pp. 1-6, May, 2015. [80] L. Laughling, M.A. Beach, K.A. Morris and J.L. Haine, “Electrical
Balance Isolation for Flexible Duplexing in 5G Mobile devices,” IEEE
conf. on 5G & Beyond – Enabling Technologies and Applications, (ICCW), London. pp. 1071-1076, June, 2015.
[81] Y. Hua, “An overview of beamforming and power allocation for MIMO
relays,” in Proc. MILCOM, 2010, pp. 375–380. [82] G. D. Collins and J. Treichler, "Practical insights on full-duplex personal
wireless communications gained from operational experience in the
satellite environment," 2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE), Salt Lake City, UT, 2015, pp.
136-141.
[83] E. Grayver, R. Keating and A. Parower, "Feasibility of full duplex communications for LEO satellite," 2015 IEEE Aerospace Conference,
Big Sky, MT, 2015, pp. 1-8.
[84] M. R. Bhavani Shankar, Gan Zheng, S. Maleki and B. Ottersten, "Feasibility study of full-duplex relaying in satellite networks," 2015
IEEE 16th International Workshop on Signal Processing Advances in
Wireless Communications (SPAWC), Stockholm, 2015, pp. 560-564. [85] D. Martiñán-Otero and C. Mosquera, "Frequency reuse in dual satellite
settings: An initial evaluation of Full Duplex operation," 2015 IEEE
International Conference on Communication Workshop (ICCW), London, 2015, pp. 1663-1668.
[86] US 7228104 B2 – Adaptive Canceller for Frequency Reuse Systems.
[87] J. R. Treichler, C. R. Johnson and M. G. Larimore. Theory and Design of Adaptive Filters, Prentice Hall, 2001.
[88] S. S. Bharj, B. Tomasic, J. Turtle, R. Turner, G. Scalzi and S. Liu, "A full-
duplex, multi-channel transmit/receive module for an S-band satellite communications phased array," 2010 IEEE International Symposium on
Phased Array Systems and Technology, Waltham, MA, 2010, pp. 202-
210. [89] Y. Liu, X. Quan, W. Pan, S. Shao and Y. Tang, "Nonlinear distortion
suppression for active analog self-interference cancellers in full duplex
wireless communication," 2014 IEEE Globecom Workshops (GC Wkshps), Austin, TX, 2014, pp. 948-953.
[90] S. Premnath, D. Wasden, S. Kasera, N. Patwari, and B.
FarhangBoroujeny, “Beyond OFDM: Best-effort dynamic spectrum access using filterbank multicarrier,” IEEE/ACM Trans. Netw., vol. 21,
no. 3, pp. 869– 882, Jun. 2013. [91] M. A. Khojastepour and S. Rangarajan, "Wideband digital cancellation
for full-duplex communications," 2012 Conference Record of the Forty
Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), Pacific Grove, CA, 2012, pp. 1300-1304.
[92] E. Ahmed and A. M. Eltawil, "All-Digital Self-Interference Cancellation
Technique for Full-Duplex Systems," in IEEE Transactions on Wireless Communications, vol. 14, no. 7, pp. 3519-3532, July 2015.
[93] Y. Sung, J. Ahn, B. V. Nguyen and K. Kim, "Loop-interference
suppression strategies using antenna selection in full-duplex MIMO relays," Intelligent Signal Processing and Communications Systems
(ISPACS), 2011 International Symp. on, Chiang Mai, 2011, pp. 1-4.
[94] D. Korpi, L. Anttila, V. Syrjälä and M. Valkama, "Widely Linear Digital Self-Interference Cancellation in Direct-Conversion Full-Duplex
Transceiver," in IEEE Journal on Selected Areas in Communications, vol.
32, no. 9, pp. 1674-1687, Sept. 2014. [95] R. Askar, N. Zarifeh, B. Schubert, W. Keusgen and T. Kaiser, "I/Q
imbalance calibration for higher self-interference cancellation levels in
Full-Duplex wireless transceivers," 5G for Ubiquitous Connectivity (5GU), 2014 1st International Conference on, Akaslompolo, 2014, pp.
92-97.
[96] D. Nguyen, L. N. Tran, P. Pirinen and M. Latva-aho, "Transmission strategies for full duplex multiuser MIMO systems," 2012 IEEE
International Conference on Communications (ICC), Ottawa, ON, 2012,
pp. 6825-6829. [97] D. Korpi et al., "Advanced self-interference cancellation and
multiantenna techniques for full-duplex radios," 2013 Asilomar
Conference on Signals, Systems and Computers, Pacific Grove, CA, 2013, pp. 3-8.
[98] L. Ding, G. T. Zhou, D. R. Morgan, Z. Ma, J. S. Kenney, J. Kim and C.
R. Giardina, “A robust digital baseband predistorter constructed using memory polynomials,” IEEE Transactions on communications, 52(1),
pp.159-165, 2004.
[99] M. Jain, J.I. Choi, T. Kim, D. Bharadia, K. Srinivasan, S. Seth, et al., "Practical real-time full duplex wireless", Proc. Annu. Int. Conf. Mobile
Comput. and Netw. (ACM Mobicom 2011) Las Vegas NV Sep., pp. 301-
312, 2011. [100] L. Anttila, D. Korpi, V. Syrjala and M. Valkama, "Cancellation of power
amplifier induced nonlinear self-interference in full-duplex
transceivers", Asilomar Conf. Signals Syst. and Comput., pp. 1193-1198,
2013.
[101] S. Huberman and T. Le-Ngoc, “Self-interference Pricing for Full-Duplex
MIMO Systems,” Wireless Communications Symposium, Globecom, pp. 3902 – 3906, 2013.
[102] S. Gollakota, and D Katabi, “Zigzag decoding: combating hidden
terminals in wireless networks,” in Proc of ACM SIGCOMM, 2008, 2008, PP. 159-170.
[103] W. T. Slingsby and J. P. McGeehan, "Antenna isolation measurements for on-frequency radio repeaters", Proc. 9th Int. Conf. Antennas Propag., vol.
1, pp. 239-243.
[104] Z. Zhang, X. Wang, K. Long, A. V. Vasilakos and L. Hanzo, "Large-scale MIMO-based wireless backhaul in 5G networks," in IEEE Wireless
Communications, vol. 22, no. 5, pp. 58-66, October 2015.
[105] R. Van Nee and R. Prasad, OFDM for Wireless Multimedia Communications. Norwood, MA, USA: Artech House, 2000.
[106] E. Everett, D. Dash, C. Dick and A. Sabharwal, "Self-interference
cancellation in multi-hop full-duplex networks via structured signaling," Communication, Control, and Computing (Allerton), 2011
49th Annual Allerton Conference on, Monticello, IL, 2011, pp. 1619-
1626. [107] T. Taniguchi and Y. Karasawa, "Design and analysis of MIMO multiuser
system using full-duplex multiple relay nodes," Wireless Days (WD),
2012 IFIP, Dublin, 2012, pp. 1-8. [108] T. Riihonen, S. Werner, and R. Wichman, “Spatial loop interference
suppression in full-duplex MIMO relays,” in Proc. Asilomar Conf.
Signals, Syst. Comput., Nov. 2009, pp. 1508–1512. [109] P. Larsson and M. Prytz, “MIMO on-frequency repeater with self-
interference cancellation and mitigation,” in Proc. IEEE 69th VTC—
Spring, 2009, pp. 1–5. [110] Edited by S. Sesia, I. Toufik and M. Baker, ‘LTE – The UMTS Long Term
Evolution: From Theory to Practice.’ Sussex, United Kingdom. Wiley,
2009. [111] 3GPP TR 36.814 v9.0.0 (2010-03), 3rd Generation Partnership Project;
Technical Specification Group Radio Access Network: Evolved Universal Terrestrial Radio Access (E-UTRA); further advancements for
E-UTRA Physical layer aspects (Release 9). [Online]. Available:
http://www.qtc.jp/3GPP/Specs/36814-900.pdf. [112] A. Gupta and R. K. Jha, "A Survey of 5G Network: Architecture and
Emerging Technologies," in IEEE Access, vol. 3, no. , pp. 1206-1232,
2015. [113] F. R. Yu, X. Zhang, and V. C. M. Leung, Green Communications and
Networking. New York. NY, USA: CRC Press, 2012
[114] DUPLO, “Design and measurement report for RF and antenna solutions for self-interference cancellation,” DUPLO Deliverable 2.1, Surrey, U.K.
[Online] Available: http://www.fp7-duplo.eu/index.Php/ deliverables.
[115] M. Al-Imari, M. Ghoraishi, P. Xiao and R.Tafazolli, “Game Theory Based Resource Allocation for Full-Duplex Systems,” IEEE 81st
Vehicular Technology Conference, VTC spring, 2015.
[116] X. Li, T. Jiang, S. Cui, J. An and Q. Zhang, "Cooperative communications based on rateless network coding in distributed MIMO
systems," in IEEE Wireless Communications, vol. 17, no. 3, pp. 60-67,
June 2010.